System and method for SPO2 instability detection and quantification

ABSTRACT

The disclosed embodiments relate to a system and method for analyzing data. An exemplary method comprises the acts of receiving data corresponding to at least one time series, and computing a plurality of sequential instability index values of the data. An exemplary system comprises a source of data indicative of at least one time series of data, and a processor that is adapted to compute at least one of a plurality of sequential instability index values of the data.

FIELD OF THE INVENTION

This invention relates to an object based system for the organization,analysis, and recognition of complex timed processes and the analysis,integration and objectification of time series outputs of data sets andparticularly physiologic data sets, and to the evaluation of thefinancial and physiologic datasets and the determination ofrelationships between them.

BACKGROUND

The analysis of time series data is widely used to characterize thebehavior of a system. The following four general categories ofapproaches are commonly applied to achieve characterization of such asystem and these provide a general background for the present invention.The approaches are illustrative both in their conceptualization,application, and limitations.

The first such approach represents a form of mathematical reductionismof the complexity through the application of a cascade of rules based onan anticipated relationship between the time series output and a givenset of system mechanisms. In this approach the operative mechanisms,data set characteristics, and intruding artifact are a priori defined tothe best extent possible. Then a set of rules is applied to characterizeand analyze the data set based on predicted relationships between thedata set and the systems being characterized. Such systems often includecascading branches of decision-based algorithms, the complexity of whichincrease greatly in the presence of multiple interactive mechanisms. Thereductionism approach is severely limited by the uncertainty andcomplexity, which rapidly emerges when a cascade of rules is applied toa highly interactive data set, when the signal to noise ratio is low,and/or when multiple data sets generated by complex and dynamicallyinteractive systems are evaluated. These methods become inordinatelymore cumbersome as the complexity and number of time series increases.In addition the subtlety of the interactive and dynamic relationshipsalong and between datasets and the variations associated with thetechnique or tools of data collection often makes the cascading rulesvery difficult to define a priori.

The weakness of simplification the analysis through mathematicalreductionism to adequately characterize the complex systems generatingsuch data sets, led to the perception that this failure resulted fromspecific limitations of a particular data format (usually the timedomain format). In other words, the time series was perceived to containsufficient information to characterize the system but, it was thought,that the recognition of this information required reformatting into adifferent mathematical representation, which emphasized other hiddencomponents which were specific for certain important systemcharacteristics. This approach is exemplified by frequency processingmethods, which reformat the time series into frequency components, suchas its sine components or wavelets, with the hope that patterns ofspecific frequency relationships within the system will emerge to berecognized. While often uncovering considerable useful information, thisapproach is remains quite limited when applied to highly complex andinteractive systems, because many complex relationships are poorlycharacterized by their frequency components, and it is often difficultto relate an output derived from frequency-based primitives to specificmechanisms operative within the system. In other words, the advantagesassociated with mathematically defined linkages between systemmechanisms and the rules based analysis provided by reductionism isreduced by the data reformatting process for the purpose of frequencybased signal processing as, for example, is provided by Fourier orwavelet transforms.

A third approach seeks to identify the patterns or relationships byrepetitively reprocessing the time series with a set of generalcomparative rules or by statistical processing. As with the datareformatting approach, the utility of this method in isolation (asembodied in neural network based analysis), is severely limited bydissociation of the output from the complex and interactive operativemechanisms, which define the output. With such processing, the relevantscope and characterization of the relationships of the output to theactual behavior of the dynamic interactions of the system is often quitelimited. This limits the applicability of such processing inenvironments wherein the characterization of behavior of the system as afunction by the output may be as important as the actual output valuesthemselves.

A fourth approach has been to apply chaotic processing to the timeseries. Again, like that of conventional signal processing thisalternative method is applied the expectation that some predictivepattern will emerge to be recognized. This technique shares several ofthe limitations noted for both frequency and statistical based datareformatting. In addition as, will be discussed, the application of thistype of processing to physiologic signals is limited by, redundant andinteractive higher control which greatly limits the progression of thesystem to a state of uncontrolled chaotic behavior. Such systems operatein environments of substantial interactive control until the developmentof a severe disease state, a point at which the diagnostic informationprovided by processing often has less adjective utility relevant timelyintervention.

The human physiologic system derives a large array of time seriesoutputs, which have substantial relevance when monitored over a finitetime interval. The human can be considered the prototypic complexinteractive system. These interactions and the mechanisms defining, themhave been the subject of intense research for over one hundred years andmost of this work has been performed the time domain. For this reasonany approach toward the characterization of such a system needs toconsider the value of engaging the body of knowledge, which relates tothese mechanisms. This has been one of the reasons that the reductionismhas predominated in the analysis of physiologic signals. U.S. Pat. Nos.5,765,563 to Vander Schaff, 5,803,066 to Rapoport, and 6,138,675 toBerthon-Jones show such simple cascade decision systems for processingphysiologic signals. U.S. Pat. No. 5,751,911 to Goldman shows areal-time waveform analysis system, which utilizes neural networks toperform various stages of the analysis. U.S. Pat. No. 6,144,877 toDepetrillo shows a processor based method for determining statisticalinformation for time series data and for detecting a biologicalcondition of a biological system from the statistical information. U.S.Pat. Nos. 5,782,240 and 5,730,144 to Katz shows a system, which applychaos analysers, which generate a time series, vector representation ofeach monitored function and apply chaotic processing to identify certainevents. All of these systems are deficient in that they are not able toadequately organize, order and analyze the true state of dynamicinteraction operative in the generation of these signals.

Critical illness is one example of a dynamic timed process, which ispoorly characterized by the above noted conventional methods. When humanphysiologic stability is under threat, it is maintained by a complexarray of interactive physiologic systems, which control the criticaltime dependent process of oxygen delivery to the organism. Each system(e.g. respiratory, cardiac or vascular) has multiple biochemical and/ormechanical controls, which operate together in a predictable manner tooptimize oxygen delivery under conditions of threat. For example anincreased oxygen requirement during infection causes the patient toincrease oxygen delivery by lowering lung carbon dioxide throughhyperventilation and the fall in carbon dioxide then causes thehemoglobin molecule to increase its affinity for oxygen thereby furtherenhancing oxygen delivery. In addition to the basic control of a singlesystem, other systems interact with the originally affected system toproducing a predictable pattern of response. For example, in thepresence of infection, the cardiac system interacts with the respiratorysystem such that both the stroke volume and heart rate increase. Inaddition, the vascular system may respond with a reduction in arterialtone and an increase in venous tone, thereby both reducing impedance tothe flow of oxygen to the tissues and shifting more blood into thearterial compartment.

Each system generally also has a plurality of predicable compensationresponses to adjust for pathologic alteration or injury to the systemand these responses interact between systems. For example thedevelopment of infectious injury to the lung will result in an increasein volume of ventilated gas to compensate for the loss of functionalsurface area. This increase in ventilation can then induce a synergisticincrease in both stroke volume and heart rate.

Finally a pathologic process altering one system will generally alsoinduce an alteration in one or more other systems and these processesare all time dependent. Sub acute or acute life threatening conditionssuch as sepsis, pulmonary embolism, or hemorrhage generally affect thesystems in cascades or predictable sequences which may have a timecourse range from as little as 20 seconds or more than 72 hours. Forexample, the brief development of airway collapse induces a fall inoxygen saturation, which then causes a compensatory hyperventilationresponse, which causes a rise in heart rate over as little as 20-30seconds. An infection, on the other hand, has a more prolonged timecourse inducing a rise in respiration rate, a rise in heart rate, andthen a progressive fall in oxygen saturation and finally a fall inrespiration rate and a finally a terminal fall in heart rate often overa course of 48-72 hours.

It can be seen therefore that each disease process engaging the organismcauses the induction of a complex and interactive time series ofpathophysiologic perturbation and compensation. At the onset of thedisease (such as early in the course of infection) the degree ofphysiologic change may be very slight and limited to one or twovariables. As a disease progresses both the magnitude of perturbationand the number of system involved increases. In addition to inducing apredictable range of perturbation, a particular disease processgenerally produces a specific range of progression and pattern ofevolution as a function of injury, compensation, and system interaction.Furthermore, this multi-system complexity, which can be induced byinitial pathologic involvement of a single system, is greatly magnifiedwhen a plurality of pathologic processes is present.

Despite the fact that these conditions represent some of the mostimportant adversities affecting human beings, these pathologic processesare poorly characterized by even the most sophisticated of conventionalmonitors, which greatly oversimplify the processing and outputs. Perhapsthis is due to the fact that this interactive complexity overwhelmed thedevelopers of substantially all of the conventional physiologicsignal-processing methods in the same way that it overwhelms thephysicians and nurses at the bedside everyday. Hospital critical carepatient monitors have generally been applied as warning devices uponthreshold breach of specific critical parameters with the focus on thebalance between timely warning of a potentially life threateningthreshold breach and the mitigation of false alarms. However, during thepivotal time, early in the process of the evolution of critical illness,the compensatory responses limit the change in primary criticalvariables so that the user, monitoring these parameters in isolation, isoften given a false sense of security. For this reason it cannot beenough to recognize and warn of the occurrence of a respiratory arrest,or hypotension, or hypoxia, or of a particular type of cardiacarrhythmia. To truly engage and characterize the processes present, apatient monitor must have capability to properly analyze, organize, andoutput in a quickly and easily understood format the true interactivestate of critical illness. As discussed below, it is one of the purposesof the present invention to provide such a monitor.

DESCRIPTION OF THE DRAWINGS

FIG. 1 a is a diagram of a three-dimensional cylindrical data matrix inaccordance with embodiments of the present invention comprisingcorresponding, streaming, time series of objects from four differenttimed data sets;

FIG. 1 b is a diagram of a portion of the diagram shown in FIG. 1 acurved back upon itself to show the flexibility of object comparisonbetween levels and different data set within the same time period andacross different levels of different data sets at different time periodsto identify a dynamic pattern of interaction between the data sets inaccordance with embodiments of the present invention;

FIG. 2 a is a diagram of a three-dimensional representation ofcollective confirmation of corresponding time series of objects of pulse(which can be heart rate and/or pulse amplitude), oxygen saturation,airflow, chest wall movement, blood pressure, and inflammatoryindicators during early infection, organized in accordance withembodiments of the present invention;

FIG. 2 b is a diagram of a representation of the dynamic multi-parameterconfrontation shown in FIG. 2 a, but extended through the evolution ofseptic shock to the death point;

FIG. 3 a is a diagram of a time series of raw data points;

FIG. 3 b is a diagram of a time series of dipole objects;

FIG. 3 c is a diagram of a time series of a slope set of the dipoleobjects shown in FIG. 3 b with the spatial attributes of the pointsremoved to highlight relative change in accordance with embodiments ofthe present invention;

FIG. 3 d is a diagram of a time series with critical boundary pointsfrom which the wave pattern can be segmented and the objects can bederived and associated properties calculated in accordance withembodiments of the present invention;

FIG. 3 e is a diagram of a time series of trend parameters calculated toprovide the trend (or polarity) analysis in accordance with embodimentsof the present invention;

FIG. 3 f is a diagram of a wave pattern shown in FIG. 3 d, which can bederived from the utilization of user-defined object boundaries inaccordance with embodiments of the present invention;

FIG. 3 g is a diagram of a representation for the manipulation by theuser for slope deviation specification in accordance with embodiments ofthe present invention;

FIG. 4 is a graphical representation of an organization of the waveformsshown in FIGS. 3 a-3 g into ascending object levels in accordance withembodiments of the present invention;

FIG. 5 a is a diagram of a cyclic process of sleep apnea that shows thecomplexity of the mechanisms defining the timed interactions ofphysiologic systems induced by upper airway instability, which may bereferred to as an “apnea cluster reentry cycle”;

FIG. 5 b is a diagram of a raw data set comprising a plurality ofsignals derived from the mechanism shown in FIG. 5 a and from which,according to embodiments of the present invention, may be represented asmulti-signal three-dimensional hierarchal object as shown in FIG. 5 a;

FIG. 5 c is a diagram showing a representation of a portion of amulti-signal object as derived from the multiple corresponding timeseries of FIG. 5 b with three multi-signal recovery objects up to thecomposite object level identified for additional processing according toembodiments of the present invention;

FIG. 6 a is a three-dimensional graphical representation of an outputfor clinical monitoring for enhanced representation of the dependent anddynamic relationships between patient variables, which may be referredto as a “monitoring cube”;

FIG. 6 b is a two-dimensional graphical representation of an output ofthe “monitoring cube” during a normal physiologic state;

FIG. 6 c is a two-dimensional graphical representation of an output ofthe “monitoring cube” showing physiologic convergence during an episodeof volitional hyperventilation;

FIG. 6 d is a two-dimensional graphical representation of an output ofthe “monitoring cube” showing pathophysiologic divergence as withpulmonary embolism;

FIG. 6 e is a two-dimensional graphical representation of an output ofthe “monitoring cube” showing a concomitant increase in blood pressureand heart rate, the cube being rotated in accordance with embodiments ofthe present invention to see which increase came first;

FIG. 7 is a schematic of a processing system for outputting and/ortaking action based on the analysis of the time series processing inaccordance with embodiments of the present invention;

FIG. 8 is a schematic of a monitor and automatic patient treatmentsystem in accordance with embodiments of the present invention;

FIG. 9 is a graphical representation of corresponding data at the rawdata level of airflow and oxygen saturation wherein a subordinatesaturation signal segment demonstrates physiologic convergence withrespect to the primary airflow signal segment;

FIG. 10 is a graphical representation of the raw data level of FIG. 9converted to the composite level, the data comprising a time series ofsequential composite objects derived from the data sets of airflow andoxygen saturation signals;

FIG. 11 is a graphical representation of a selected compositesubordinate object of oxygen saturation from FIG. 10 matched with itscorresponding primary composite object of airflow, as they are stored asa function of dipole datasets in a relational database, object databaseor object-relational database in accordance with embodiments of thepresent invention;

FIG. 12 is a graphical representation of a comparison between two datasets of airflow wherein at the fundamental level the second data setshows evidence of expiratory airflow delay during the recovery object,wherein the recovery object is recognized at the composite level inaccordance with embodiments of the present invention;

FIG. 13 is a diagram of a schematic object mapping at the compositelevel of corresponding signals of airflow and oxygen saturation inaccordance with embodiments of the present invention;

FIG. 14 is a diagram of a schematic object mapping at the compositelevel of two simultaneously measured parameters with a region ofanticipated composite objects in accordance with embodiments of thepresent invention;

FIG. 15 is a diagram of a schematic object mapping and scoring at thecomposite level of two simultaneously measured parameters with theregion of anticipated composite objects in accordance with embodimentsof the present invention;

FIG. 16 is a diagram of a system for customizing a constant positiveairway pressure (CPAP) auto-titration algorithm based on the analysis ofmultiple corresponding signals in accordance with embodiments of thepresent invention;

FIG. 17 is a diagram of a system for comparing multiple signals andacting on the output of the comparison in accordance with embodiments ofthe present invention;

FIG. 18 is a block diagram of a spirocapnoximetry system in accordancewith an exemplary embodiment of the present invention; and analyzed inaccordance with an exemplary embodiment of the present invention;

FIGS. 20 and 21 are graphs showing exemplary segments of a time seriesand expanded snapshots of different portions of data represented by thetime series segment;

FIG. 22 is a graph showing a time series, along with a plurality ofthumbnails in accordance with an exemplary embodiment of the presentinvention;

FIG. 23 is a block diagram of a hierarchical channel object inaccordance with an exemplary embodiment of the present invention; and

FIG. 24 is a block diagram of a hierarchical time series object inaccordance with an exemplary embodiment of the present invention.

DETAILED DESCRIPTION

The present invention comprises a system and method of providingcomprehensive organization and analysis of interactive complexity alongand between pluralities of time series. An embodiment of the presentinvention comprises an object-based method of iterative relationalprocessing of time series fragments or their derivatives along andbetween corresponding time series. The system then applies an iterativecomparison process of those fragments along and between a plurality timeseries. In this way, the relationship of a wide range of characteristicsof substantially any dynamic occurrence in one time series can becompare to the same or other characteristics of substantially anydynamic occurrence along another portion of the same time series or anyof the processed corresponding time series.

In accordance with embodiments of the present invention, a first timeseries is processed to render a time series first level derived fromsequential time series segments the first series, the time series firstlevel is stored in a relational database, object database orobject-relational database. The first time series level is processed torender a second time series level derived from the sequential timeseries component of the first time series level and these are stored inthe relational database, object database or object-relational database.Additional levels are then derived as desired. The compositions ofsequential time series, which make up the first and second levels, aredetermined by the definitions selected for the respective segments fromwhich each level is derived. Each time series fragment is represented asa time series object, and each more complex time series object inheritsthe more basic characteristics of time series objects from which theyare derived.

The time course of sub acute and acute critical illness to point ofdeath is highly variable and can range from 24-72 hours with toxicshock, to as little as 30 seconds with neonatal apnea. The presentinventors recognized that, regardless of its time course, such apathological occurrence will have a particular “conformation”, whichaccording to the present invention can be represented spatially by anobject-based processing system and method as a particular object or timeseries of objects, as a function of the specific progression of theinteractive components for the purpose of both processing and animation.The present inventors also recognized that the development of such aprocessing system would be capable of organizing and analyzing theinordinate degree of dynamic complexity associated with the output fromthe biologic systems through the automatic incorporation of these timeseries outputs into a highly organized relational, layered, object baseddata structure. Finally, the inventors further recognized that becauseof the potentially rapid time course of these illnesses and theirreversible endpoint, that patient care monitors must provide a quicklyand easily understood output, which gives the medical personnel asimplified and succinct analysis of these complex relationships whichaccurately reflects the interactive complexity faced by the patient'sphysiologic systems.

It has been suggested that the development of periodicity in a humanphysiologic system represents a simplification of that system. Thisconcept is based on the perception that the human interactivephysiologic systems operates in an environment of chaos and that apartial loss of control, simplifies the relationships, allowing simplerperiodic relationships to emerge. However, there is considerable reasonto believe that this is not the case. Patients centering an environmentof lower partial pressure of oxygen, as at altitude, will developperiodicity of ventilation. This does not indicate a generalsimplification of the system but rather, one proposed operativemechanism for the emergence of this new pattern is that the patternreflects the uncovering of a preexisting dynamic relationship betweentwo controllers, which now, together determine ventilation in this newenvironment. At sea level, the controller responding to oxygen wassubordinate the controller responding to carbon dioxide so that theperiodicity was absent. This simple illustration serves to demonstratethe critical linkage between patient outputs and higher control and thecriticality of comprehensively comparing dynamic relationships along andbetween signals to achieve a true picture of the operative physiology.While periodicities are, at times, clearly pathologic, their developmentin biologic systems, rather than a manifestation of simplification ofphysiological behavior often represents the engagement of morerudimentary layers of protection of a particular organ function or rangebuilt into the control system. This illustration further demonstratesthat a given physiologic signal, when monitored in isolation, may appearto exhibit totally unpredictable and chaotic behavior, but whenconsidered in mathematical or graphical relation (as in phase space) toa plurality of corresponding interactive signals, and to the interactivecontrol mechanisms of those corresponding signals, the behavior of theoriginal, chaotic appearing, signal often becomes much more explicable.

By way of example, consider a timed plot of oxygen saturation (SPO₂)under heavy sedation during sleep. This state is often associated with aloss of the maintenance of a narrow control range of ventilation duringsleep and with the loss of stability of the airway so that a plot of theoxygen saturation, in the presence of such deep sedation, shows a highlyvariable pattern, which often appears grossly unpredictable, withsustained falls in oxygen saturation intermixed with rapid falls andoften seemingly random rapid corrections. However, there are definablelimits or ranges of the signal, and generally definable patterns, whichare definable within the background of a now highly variable SPO₂signal. It may be tempting to define this behavior statistically or by achaotic processor in the hope of defining some emerging patterns as afunction of the mathematical behavior of that signal. However, whenanalyzed with the partial pressure of CO₂, the minute ventilation, and aplot of EEG activity the oxygen saturation values are seen as asubordinate signal to the airflow which is now being controlled by adysfunctional control process, which process is being salvaged by a morecoarse and rudimentary survival response mechanism such as an arousalresponse. The apparently chaotic behavior is now seen as driven by acomplex but predictable sequence of a plurality of dynamic interactiverelationships between corresponding signals and the forces impactingthem. Therefore, in the presence of a pathophysiologic process, thebehavior and ranges of any given signal are optimally defined by thedynamic patterns of the interactive behavior of corresponding signalsand their respective dynamic ranges.

A biologic system actually exploits the chaotic output of simplenonlinear relationships by defining control ranges, which are affectedby variations in corresponding signals. This produces a great degree indiversity of dynamic physiologic response, which is beneficial in thatit may favor survival of a particular subgroup, in the presence of acertain type of pathophysiologic threat. The present inventors notedthat, while this diversity imparts greater complexity, this complexitycan be ordered by the application of iterative processing in which agiven signal is defined as a function of a range “dynamic normality.”According to one embodiment of the present invention, each signal isdefined as a function of its own dynamic range (and in relation to apredicted control range) and as a function of contemporaneously relevantrelationships of the dynamic ranges of other corresponding signals (withrespect to their respective control ranges).

Embodiments of the present invention may comprise a system and methodfor organizing and analyzing multiple time series of parametersgenerated by a patient (as during critical illness) and outputting thisanalysis in readily understandable format. The system may include thecapability of simultaneously processing dynamic time series ofphysiologic relationships in real time at multiple levels along eachparameter and across multiple levels of different parameters.Embodiments of the present invention provide this level of interactiveanalysis specifically to match the complexity occurring during apathologic occurrence. More specifically, embodiments of the presentinvention may provide an analysis system and method that analyzes thetrue dynamic state of a biologic system and the interactive primary andcompensatory perturbations defining that state. During health the outputof physiologic systems are maintained within tight variances. As will bediscussed, a signal processing system in accordance with embodiments ofthe present invention may expose the extent to which the signals areheld within these tight variances and may be characterized as a functionof their dynamic ranges of variance. The signals may be furthercharacterized as a function their dynamic relationships along the timeseries within a given signal and between a plurality of additionalcorresponding signals. A monitor of the human physiologic state duringcritical illness in accordance with embodiments of the present inventionmay be adapted to analyze time series relationships along and between aplurality signals with the similar degree of analytic complexity as isoperative in the biologic systems controlling the interactive responseswhich are inducing those signals and of outputting an indication basedon the analysis in a readily understandable format. Such a format maycomprise a dynamic format such as a two-dimensional or three-dimensionalobject animation, the configuration of which is related to the analysisoutput. The configurations of the animation changes with the analysisoutput, as this output changes over time in relation to changes in thepatient's physiologic state. The animation thereby provides a succinctand dynamic summary rendering which organizes the complexity of theinteractive components of the output so that they can be more readilyunderstood and used at the bedside and for the purpose of patientmanagement and education of medical staff relevant the application oftime series analysis in the assessment of disease. According to anexemplary embodiment of the present invention the process proceeds byorganizing the multiple data streams defining the input into a hierarchyof time series objects in an object based data structure, analyzing andcomparing objects along and across time series, organizing andsummarizing the output, animating and presenting the summarized outputand taking action based on the output. Embodiments of the presentinvention may comprise analyzing and comparing new objects derivedsubsequent the previous actions, adjusting the action and repeating theprocess. Additionally, embodiments of the present invention may comprisecalculating the expense and resource utilization related to said output.

In accordance with embodiments of the present invention, a plurality oftime series of physiologic signals (including timed laboratory data) ofa given physiologic process (such as sepsis) can have a particularconformational representation in three-dimensional space (as is shown inFIGS. 2 a and 2 b). This spatial representation comprises a summary ofthe relational data components, as analyzed, to diagnose a specificpathophysiologic process, to determine its progression, to define itsseverity, to monitor the response to treatment, and to simplify therepresentative output for the health care worker.

Two exemplary pathophysiologic processes (airway instability and sepsis)will be discussed below and exemplary patient monitoring systems andmethods according to the present invention, for processing, organizing,analyzing, rendering and animating output, and taking action (includingadditional testing or treatment based on said determining) will bedisclosed.

An important factor in the development of respiratory failure is airwayinstability, which results in air-way collapse during sedation, stroke,narcotics, or stupor. As illustrated in FIGS. 5 a and 5 b, such collapseoccurs in dynamic cycles called apnea clusters affecting a range ofphysiologic signals. These apnea clusters are an example of a common andpotentially life threatening process, which, perhaps due to the dynamicinteractive complexity of the time series, is not recognized byconventional hospital processors. Yet subgroups of patients in thehospital are at considerable risk from this disorder. Patients withotherwise relatively, stable airways may have instability induced bysedation or narcotics and it is critical that this instability berecognized in real time in the hospital so that the dose can be adjustedor the drug withheld upon the recognition of this development.Conventional patient monitors are neither configured to provideinterpretive recognition the cluster patterns indicative of airway andventilation instability nor to provide interpretative recognition of therelationship between apnea clusters. In fact, such monitors often applyaveraging algorithms, which attenuate the clusters. For these reasonsthousands of patients each day enter and leave hospital-monitored unitswith unrecognized ventilation and airway instability.

Conventional hospital-based central patient monitors such as AgilentCMS, or the GE-Marquette Solar 8000, do not automatically detect andquantify obstructive sleep apnea or the cluster patterns indicative ofairway instability. Because sleep apnea is so common, it is possiblethat many patients who unknowingly have sleep apnea have passed throughhospitals over the past decade without being diagnosed. Many of thesepatients may never be diagnosed in their lifetime, which could result inincreased suffering and medical costs. Also, other patients may developcomplications while in the hospital due to the failure to recognizeobstructive sleep apnea or airway instability. If automatic detection ofsleep apnea is not performed, an opportunity to improve the efficiencyof the diagnosis of obstructive sleep apnea, and to increase the revenuefor the critical care monitoring companies marketing may remainunrealized. Further, an opportunity to increase hospital and/orphysician revenue has been missed. Automatic detection of airwayinstability and/or obstructive sleep apnea by observing data clustersindicative of those conditions may reduce the occurrences of respiratoryfailure, arrest, and/or death related to the administration of IVsedation and narcotics to patients in the hospital with unrecognizedairway instability.

The importance of recognizing airway instability in real-time may beappreciated by those of ordinary skill in the art based on considerationof the combined effect that oxygen therapy and narcotics or sedation mayhave in the patient care environment in the hospital. By way of example,consider the management of a post-operative obese patient after upperabdominal surgery. Such a patient may be at particular risk forincreased airway instability in association with narcotic therapy in thefirst and second post-operative day due to sleep deprivation, airwayedema, and sedation. Indeed, many of these patients have significantsleep apnea prior to admission to the hospital which is unknown to thesurgeon or the anesthesiologist due to the subtly of symptoms. Thesepatients, even with severe sleep apnea, may be relatively safe at homebecause of an arousal response. However, in the hospital, narcotics andsedatives often undermine the effectiveness of the arousal response. Theadministration of post-operative narcotics can significantly increaseairway instability and, therefore, place the patient at risk. Many ofthese patients are placed on electrocardiographic monitoring but thealarms are generally set at high and low limits. Hypoxemia, induced byairway instability generally does not produce marked levels oftachycardia; therefore, airway instability is poorly identified byelectrocardiographic monitoring without the identification of specificclusters of the pulse rate. In addition, oximetry evaluation may also bea poor method of identifying airway instability if an averaginginterval, which may result in the attenuation of dynamic desaturations,is employed. Even when clustered desaturations occur, they may bethought to represent false alarms if they are brief. When desaturationsare recognized as potentially real, a frequent result is theadministration of nasal oxygen by a caregiver, which may produceundesirable results. For example, nasal oxygen may prolong the apneasand potentially increase functional airway instability. From amonitoring perspective, the addition of oxygen therapy can be seen topotentially hide the presence of significant airway instability byattenuation of the level of desaturation and reduction in theeffectiveness of the oximeter as a monitoring tool in the diagnosis ofthis disorder.

Oxygen and sedatives can produce undesirable results in patients withseverely unstable airways since the sedatives increase the apneas andthe oxygen hides them from the oximeter. For all these reasons, as willbe shown, according to the present invention, it is important to monitorand identify specific cluster patterns indicative of airway instabilityor sleep apnea. This may be particularly true during the administrationof narcotics or sedatives in patients with increased risk of airwayinstability.

The central drive to breathe, which is suppressed by sedatives ornarcotics, basically controls two muscle groups. The upper airway“dilator muscles” and the diaphragm “pump muscles”. The tone of boththese muscle groups must be coordinated. A fall in tone from the braincontroller to the airway dilators results in upper airway collapse.Alternatively, a fall in tone to the pump muscles causeshypoventilation.

Two major factors contribute to respiratory arrest in the presence ofnarcotic administration and sedation. The first and most traditionallyconsidered potential effect of narcotics or sedation is the suppressionof the drive to the pump muscles. In this situation, airway instabilitymay be less important than the reduced stimulation of the pump muscles,such as the diaphragm and chest wall, resulting in inadequate tidalvolume, which results in an associated fall in minute ventilation and aprogressive rise in carbon dioxide levels. The rise in carbon dioxidelevels causes further suppression of the arousal response, therefore,potentially causing respiratory arrest. This first cause of respiratoryarrest associated with sedation or narcotics has been the primary focusof previous efforts to monitor patients postoperatively for the purposeof minimization of respiratory arrests. Both oximetry and tidal CO₂monitoring have been used to attempt to identify and prevent thisdevelopment. However, in the presence of oxygen administration, oximetryis likely to be a poor indicator of ventilation. In addition, patientsmay have a combined cause of ventilation failure induced by the presenceof both upper airway instability and decreased diaphragm output. Inparticular, the rise in CO₂ may increase instability of the respiratorycontrol system in the brain and, therefore potentially increase thepotential for upper airway instability.

The second factor causing respiratory arrest due to narcotics orsedatives relates to depression of drive to upper airway dilator musclescausing a reduction in upper airway tone. This reduction in airway toneresults in dynamic airway instability and precipitates cluster cycles ofairway collapse and recovery associated with the arousal response as thepatient engages in a recurrent and cyclic process of arousal basedrescue from each airway collapse. If, despite the development of asignificant cluster of airway collapses, the narcotic administration orsedation is continued, this can lead to further prolongation of theapneas and eventual respiratory arrest. There is, therefore, a dynamicinteraction between suppression of respiratory drive, which results inhypoventilation, and suppression of respiratory drive, which results inupper airway instability. At any given time, a patient may have agreater degree of upper airway instability or a greater degree ofhypoventilation. The relative combination of these two events willdetermine the output of the monitor, with the former producing a simpletrending rise (as with end tidal CO₂) or fall (as with minuteventilation or oxygen saturation) and the latter producing a clusteroutput pattern.

Unfortunately, this has been one of the major limitations of carbondioxide monitoring. The patients with significant upper airwayobstruction tend to be the same patients who develop significanthypoventilation. The upper airway obstruction may result in drop out ofthe nasal carbon dioxide signal due to both the upper airwayobstruction, on one hand, or be due to conversion from nasal to oralbreathing during a recovery from the upper airway obstruction, on theother hand. Although breath by breath monitoring may show evidence ofapnea, conversion from nasal to oral breathing can reduce the ability ofthe CO₂ monitor to identity even severe hypoventilation in associationwith upper airway obstruction, especially if the signal is averaged orsampled at a low rate. For this reason, conventional tidal CO₂monitoring when applied with conventional monitors may be leasteffective when applied to patients at greatest risk, that is, thosepatients with combined upper airway instability and hypoventilation.

As described in U.S. Pat. No. 6,223,064 (assigned to the presentinventor and incorporated herein by reference), the underlying cyclicphysiologic process, which drives the perpetuation of a cluster ofairway closures, can be exploited to recognize upper airway instabilityin real time. The underlying cyclic process, which defines the behaviorof the unstable upper airway, is associated with precipitous changes inventilation and attendant precipitous changes in monitored parameters,which reflect and/or are induced by such ventilation changes. Forexample, cycling episodes of airway collapse and recovery producessequential precipitous changes in waveform output defining analogouscluster waveforms in the oximetry pulse tracing, the airflow amplitudetracing, the oximetry SpO₂ tracing, the chest wall impedance tracing andthe EKG pulse rate or R to R interval tracing.

The use of central hospital monitors generally connected to a plurality(often five or more) of patients through telemetry is a standardpractice in hospitals. The central monitor is not, however, typicallyinvolved in the diagnosis of sleep apnea, for which the application ofadditional monitors is needed. The present inventors are not aware ofany of the central patient monitors (such as those in wide use whichutilize central telemetry), which provide the above functionality. Theuse of additional monitors to diagnose sleep apnea is inefficientbecause it requires additional patient connections, is not automatic,and is often unavailable. According to one aspect of the presentinvention, the afore-referenced conventional hospital monitors may beadapted to provide a measurement and count of airflow attenuation and/oroxygen desaturation and to compare that output with the chest wallimpedance to routinely identify the presence of obstructive sleep apneaand to produce an overnight summary and formatted output. The summaryand formatted output, which may be over read by the physician, may meetthe standard of the billing code in that it includes airflow, oximetry,chest impedance, and EKG or body position. Embodiments of the presentinvention may use conventional apnea recognition algorithms (as are wellknown in the art), such as the apnea recognition system of U.S. Pat. No.6,223,064 (hereby incorporated by reference), or another suitable systemfor recognizing sleep apnea.

The present inventors discovered and recognized that the addition ofsuch functionality to central hospital monitors could result in improvedefficiency, patient care, reduced cost and potentially enhancedphysician and hospital revenue. The business of diagnosing sleep apneahas long required additional equipment relative to the standard hospitalmonitor and would be improved by the conversion and programming ofcentral hospital monitors to provide this functionality. Moreover, themethod of using the processor of a central hospital monitor tointeractively detect obstructive sleep apnea and provide processor-basedinterpretive indication of obstructive output and to output a summarysuitable for interpretation to make a diagnosis of obstructive sleepapnea can result in the automatic diagnosis of sleep apnea for manypatients who may be unaware of their condition. The present inventionmay also allow patient monitoring companies, which manufacture thecentral hospital monitors, to enter the sleep apnea diagnostic marketand to exploit that entry by providing a telemetry connection ofpositive pressure devices to the primary processor or secondaryprocessor of the carried telemetry unit so that positive pressure can beadjusted by the patient monitor. The present invention may facilitategrowth in the field of selling positive pressure devices by providing anopportunity for hospital monitoring companies to create specializedinterfaces for the transport of telemetry data between patient monitorsand/or the associated telemetry unit to the positive pressure devices.Moreover, market growth may be enhanced because more potential customersof positive pressure treatment may be identified.

According one aspect of the present invention, the recognition ofsequential precipitous changes can be achieved by analyzing the spatialand/or temporal relationships between at least a portion of a waveforminduced by at least a first apnea and at least a portion of a waveforminduced by at least a second apnea. This can include the recognition ofa cluster, which can comprise a high count of apneas with specifiedidentifying features which occur within a short time interval along saidwaveform (such as 3 or more apneas within about 5-10 minutes) and/or caninclude the identification of a waveform pattern defined by closelyspaced apnea waveform or waveform clusters. Further, the recognition caninclude the identification of a spatial and/or temporal relationshipdefined by waveform clusters, which are generated by closely spacedsequential apneas due to cycling upper airway collapse and recovery.Using the above discoveries, typical standard hospital monitors can beimproved to provide automatic recognition of apnea clusters indicativeof upper airway instability and to provide an automatic visual oraudible indication of the presence of such clusters and further toprovide a visual or audible output and severity of this disorder therebyrendering the timely recognition and diagnosis of upper airwayinstability and obstructive sleep apnea a typical occurrence in thehospital.

FIG. 5 a illustrates the re-entry process driving the propagation ofapnea clusters. The physiologic basis for these clusters has beenpreviously described in U.S. Pat. Nos. 5,891,023 and 6,223,064 (thedisclosure of each of which is incorporated by reference as ifcompletely disclosed herein). This cycle is present when the airway isunstable but the patient is capable of arousal. In this situation, inthe sleeping or sedated patient, upon collapse of the airway, thepatient does not simply die, she rescues herself and precipitously opensthe airway to recover by hyperventilation. However, if the airwayinstability remains after the arousal and rescue is over, the airwaycollapses again, only to result in another rescue event. This cycleproduces a cluster of closely spaced apneas with distinct spatial,frequency and temporal waveform relationships between and within apneaswherein the physiologic process re-enters again and again to produce aclustered output. In accordance with aspects of the present invention,an apnea cluster is comprised of a plurality (two or more) of closelyspaced apneas or hypopneas. Analysis of three or more apneas isdesirable. Embodiments of the present invention include recognition ofapnea clusters along signals derived from sensors outside the body orfrom sensors within the body, for example in association withpacemakers, catheters, or other indwelling or implanted devices orsensors wherein the signals are indicative of parameters including SpO₂,pulse (including pulse characteristics as derived for example from theplethesmographic pulse defined, for example, by a red pleth signal, anIR pleth signal, and ratio of ratios, to name a few), chest wallimpedance, airflow (including but not limited to exhaled carbon dioxide(CO₂) and air temperature (for example measured by a thermistor), andsound. Additional parameters that may be analyzed include theplethesmographic pulse, blood pressure, heart rate, ECG (including, forexample, QRS morphology, pulse rate, R to R interval plots and timedplots of ST segment position to name a few), chest wall and/or abdominalmovements, systolic time intervals, cardiac output. Additional examplesinclude continuous cardiac outputs as by CO2 analysis, chest impedance,and thermodilution, esophageal and plevd process parameters,genioglossal tone, accessory, EEG signals, EMG signals, and othersignals, that provide a cluster pattern indicative of a condition thatis of interest from a diagnostic perspective. All of these parameterscomprise respiratory parameters since they manifest, for example,circulatory, movement, electrical and electrochemical patterns ofvariations in response to respiratory patterns of variations due topathophysiologic instabilities.

The present invention further includes a system for defining thephysiologic status of a patient during critical illness based on thecomparison of a first parameter along a first monitored time intervaldefining a first timed data set to at least one other parameter along asecond time interval, defining a second timed data set. The second timeinterval corresponds to the first time interval and can actually be thefirst time interval or another time interval. The second time intervalcorresponds to the effected physiologic output of the second parameteras inclined by the output of the first parameter during the first timeinterval. For example the first time interval can be a five to fifteenminute segment of timed airflow and the time interval can be a slightlydelayed five to fifteen minute segment of timed oxygen saturationderived from the airflow which defined the dataset of the first timeinterval.

According another aspect of the present invention, the microprocessoridentifies changes in the second parameter that are unexpected inrelationship to the changes in the first parameter. For example, whenthe microprocessor identifies a pattern indicative of a progressive risein minute ventilation associated with a progressive fall in oxygensaturation, a textual warning can be provided indicating physiologicdivergence of the oxygen saturation and minute ventilation. For example,the term “divergent oxygen saturation” can be provided on the patientmonitor indicating that an unexpected change in oxygen saturation hasoccurred in association with the ventilation output. The occurrence ofsuch divergence is not necessarily a life threatening condition but canbe an early warning of significant life threatening conditions such aspulmonary embolism or sepsis. If the patient has an attached apparatuswhich allows the actual minute ventilation to be quantitatively measuredrather than trended then, divergence can be identified even when theoxygen saturation does not fall as defined by plotting the timed outputof ventilation indexing oximetry as by formulas discussed in the U.S.patent applications (of one of the present inventors) entitled MedicalMicroprocessor System and Method for Providing a Ventilation IndexedValue (U.S. Application Ser. No. 60/201,735) and Microprocessor Systemfor the Simplified Diagnosis of Sleep Apnea (U.S. application Ser. No.09/115,226) (the disclosure of each of which is incorporated herein byreference as if completely disclosed herein). Upon the identification ofdivergence, the time series of other parameters such as the temperature,while blood cell count and other lab tests can be included to identifythe most likely process causing, the divergence.

One of the reasons that the identification of pathophysiologicdivergence is important is that such identification may provide earlierwarning of disease. In addition, if the patient progresses to developsignificantly low levels of a given parameter, such as oxygen saturationor pulse, it is useful to be able to go back and identify whether or notthe patient experienced divergence of these parameters earlier sincethis can help identify whether it is a primary cardiac or pulmonaryprocess which is evolving and indeed the time course of the physiologicprocess is provided by both diagnostic and therapeutic. Consider, forexample, a patient experiencing significant drop in oxygen saturationand cardiac arrest. One purpose of the present invention is to providean output indicative of whether or not this patient experienced acardiac arrhythmia which precipitated the arrest or whether someantecedent pulmonary process occurred which caused the drop in oxygensaturation which then ultimately resulted in the cardiac arrhythmia andarrest. If the patient is being monitored by chest wall impedance,oximetry and EKG, all three parameters can be monitored for evidence ofpathophysiologic divergence. If, according to the present invention, theprocessor identifies divergence of the oxygen saturation in associationwith significant rise in minute ventilation, then consideration forbedside examination, chest x-ray, arterial blood gas measurement can allbe carried out so that the relationship between cardiac and pulmonarycompensation in this patient can be identified early rather than waitinguntil a threshold breach occurs in one single parameter. Since, with theuse of conventional monitors, threshold breach of an alarm can beseverely delayed or prevented by an active compensatory mechanism, suchas hyperventilation, one advantage of the present invention is that theprocessor can provide warning as much as four to eight hours earlier byidentifying pathophysiologic divergence rather than waiting for thedevelopment of a threshold breach.

Another example of the value of monitor based automatic divergencerecognition, according to embodiments of the present invention isprovided by a patient who has experienced a very mild breach of thealarm threshold in association with significant physiologic divergencesuch as a patient whose baseline oxygen saturation is 95% in associationwith a given baseline amplitude and frequency of minute ventilation asidentified by an impedance monitor. For this patient, the fall in oxygensaturation over a period of two hours from 95% to 89% might be perceivedby the nurse or house officer as representing only a mild change whichwarrants the addition of simple oxygen treatment by nasal cannula but nofurther investigation. However, if this same change is associated withmarked physiologic divergence wherein the patient has experiencedsignificant increase in the amplitude and frequency of the chestimpedance, the microprocessor identification of significantpathophysiologic divergence can give the nurse or house officer cause toconsider further performance of a blood gas, chest x-ray or furtherinvestigation of this otherwise modest fall in the oxygen saturationparameter.

Excessive sedation is unlikely to produce physiologic divergence sincesedation generally results in a fall in minute ventilation, which willbe associated with a fall in oxygen saturation if the patient is notreceiving nasal oxygen. The lack of pathophysiologic divergence inassociation with a significant fall in oxygen saturation can providediagnostic clues to the house officer.

In accordance with embodiments of the present invention, aprocessor-based system can automatically output an indication ofpathophysiologic divergence relating to timed data sets derived fromsensors which measure oxygen saturation, ventilation, heart rate,plethesmographic pulse, and/or blood pressure to provide automaticcomparisons of linked parameters in real time, as will be discussed. Theindication can be provided in a two or three-dimensional graphicalformat in which the corresponding parameters are presented summarygraphical format such as a timed two-dimensional or three-dimensionalanimation. This allows the nurse or physician to immediately recognizepathophysiologic divergence.

According to another aspect of exemplary embodiments of the presentinvention, the comparison of signals can be used to define amathematical relationship range between two parameters and the degree ofvariance from that range. This approach has substantial advantages overthe simple comparison of a given signal with itself along a time seriesto determine variability with respect to that signal, which has beenshown to correlate loosely with a diseased or aged physiologic system.Such an approach is described in Griffin U.S. Pat. No. 6,216,032, thedisclosure of which is incorporated by reference as is completelydisclosed herein. As appreciated by those of ordinary skill in the art,the signal variability processing method, which has been widely usedwith pulse rate, lacks specificity since variance in a given signal mayhave many causes. According to embodiments of the present invention, aplurality of signals are tracked to determine if the variability ispresent in all of the signals, to define the relationship between thesignals with respect to that variability, and to determine if aparticular signal (such as airflow, for example) is the primary (first)signal to vary with other signals tracking the primary signal. Withrespect to analysis of signal variability, airway instability, sepsis,stroke, and congestive heart failure are all associated with a highdegree of heart rate variability and this can be determined in relationto a baseline or by other known methods. In accordance with embodimentsof the present invention, the general variability of a plurality ofsignals is determined and these are matched to determine if a particularsignal has a greater variability than the other signals, and moreimportantly the dynamic relationship between the signals is determinedto identify the conformation of that variability. In this respect forexample, the pulse in sepsis in a neonate may show a high degree ofvariability, by confirming that this variability is associated with ageneral multi-parameter conformation rather than a conformation ofrapidly expanding and contracting parameters, as is typical of airwayinstability. In this way, the etiology of the pulse variability is muchbetter identified.

FIGS. 2 a and 2 b are graphical representations of parametric modelsthat may be constructed in accordance with embodiments of the presentinvention to assist in the recognition of non-conformities of a range ofparameters. The parameters, which may represent time series data, may bedefined to correspond with data that is variable in response to certainconditions such as sleep apnea or sepsis. The shape of each region ofthe geometric figures illustrated in FIGS. 2 a and 2 b may be defined torepresent a range of normal values for each parameter (oxygen saturationincluding arterial and venous), airflow, pulse, inflammation indicators,blood pressure and chest movement in FIG. 2 a) that is being evaluated.As illustrated in FIGS. 2 a and 2 b, the shape of one or more of theparametric representations may vary over time, indicating relationalnon-conformity with respect to expected normal time series data. Thedegree and pattern of divergence from the predetermined normal range mayserve to indicate the presence of a malady such as sleep apnea orsepsis. Examples of analytical tools that may be employed as at leastone component of an embodiment of the present invention include timedomain analysis, frequency domain analysis, neural network analysis,preprocessing signals to remove artifacts, phase analysis, patternrecognition, ratiometric analysis, wavelet analysis, filtering (average,median, ACF, ADC), histogram analysis (stochastic distribution),variability analysis, entropy analysis, data fusion, fractal analysistransformations, combine or convolve signals and peak detector analysis.

As illustrated in FIGS. 2 a and 2 b, variability may be defined inrelation to which parameters are changing, whether they are changingtogether in a particular category of conformation indicative of aspecific disease process, and the extent to which they followanticipated subordinate behavior is identified. According to anotheraspect of an exemplary embodiment of the present invention, the timeseries of the parameter “relationship variance” and the time series ofthe “relationship variability” may be analyzed as part of a data matrix.Those of ordinary skill in the art will appreciate that the shape of theregion representing a collection of parameters of interest may bedefined to correspond to a wide range of geometries. For example, theparametric representation may be defined to have a cross section of acircle (see, for example, FIGS. 1 a and 1 b), a rectangle or anysuitable parameter to facilitate analysis of the data representative ofthat parameter.

As illustrated in FIG. 2 a, airflow and heart rate increases begin todevelop early in the state of sepsis. In FIG. 2 a, oxygen saturationdoes not vary much outside its normal range even though airflow beginsto increase because the peak value of the oxygen saturation vale tolimited. As septic shock evolves, variability increases and the tightrelationship between airflow and oxygen saturation begins to break down(see FIG. 2 b). In one embodiment of the present invention, thisrelationship is analyzed, as time series of the calculated variance ofthe airflow, variance of the heart rate, and variance of the oxygensaturation, along with the streaming time series of objects of theoriginal measured values. Timed calculated variability thereby comprisescomponents of a data matrix of objects having a particular geometricshape. Furthermore, a time series of the variance from a givenrelationship and the variability of that variance may be derived andadded to the data matrix. By way of example, an index of the magnitudevalue of airflow in relation to the magnitude value of oxygen saturationand/heart rate is calculated for each data point (after adjusting forthe delay) and a time series of this index is derived. Then, a timeseries of the calculated variability of the index is derived and addedto the data matrix. The slope or trend of the index of “airflow” andoxygen saturation will rise significantly as septic shock evolves andthis can be correlated with the slope of the variability of that index.In comparison with septic shock, in airway instability, the time seriesof these parameters show a high degree of variability generally but arelatively low degree of variance of the indexed parameters associatedwith that variability (since despite their precipitous dynamic behavior,these parameters generally move together maintaining the basicrelationships of physiologic subordinance). In addition to heart rate, atime series of the plethesmographic pulse (as amplitude, ascendingslope, area under the curve or the like) variability and variance (aswith continuous blood pressure or airflow) can be derived andincorporated with the data matrix for analysis and comparison todetermine variability and variance relationships as well as to definethe general collective conformation of the dynamic relationships of allof these parameters.

According to another aspect of an embodiment of the present invention,the analysis of subsequent portions of a time-series can automaticallybe adjusted based on the output of the analysis of preceding portions ofa time-series. By way of example, with timed waveforms, such as SpO₂, inclinical medicine, two differing conditions may occur intermittently: afirst condition may occur in which additional processing of acquireddata is desirable intermittently with a second condition in which theadditional processing of data is not desirable. For example, theapplication of smoothing algorithms if they are not needed may result inmodification of the slope of an oxygen desaturation and the slope ofresaturation. Improper smoothing may also affect the relativerelationship between the desaturation and resaturation slopes.Embodiments of the present invention may be adapted to performadditional processing such as smoothing when it is desirable and omitthe additional processing when the additional processing is notdesirable. Subsequently, the data signal is processed with clusteranalysis technology for the recognition of airway instability. Thecluster analysis technology may be adjusted to account for the effect ofaveraging on the slopes and the potential for averaging to attenuatemild desaturations.

In an exemplary embodiment of the present invention, a microprocessorsystem is provided for the recognition of specific dynamic patterns ofinteraction between a plurality of corresponding and related timeseries. The system comprises a processor programmed to process a firsttime series to produce a lower-level time series of sequential timeseries fragments derived from the first time series, process thelower-level time series to produce a higher-level time series comprisedof sequential time series fragments from the lower-level time series,process a second time series, the second time series being related tothe first time series, produce a second lower-level time series ofsequential time series fragments derived from the second time series,and identify a dynamic pattern of interaction between the first timeseries and the second time series. The system can be further programmedto process the lower-level time series of the second time series toproduce a higher-level time series derived from sequential time seriesfragments of the second lower-level time series. The system can beprogrammed to process a third time-series, the third time series beingrelated to at least one of the first and the second time series, toproduce a third lower-level time series of sequential time seriesfragments derived from said third time series. The system can beprogrammed to process the higher-level time series to produce acomplex-level time series derived from sequential time series fragmentsof said higher-level time series.

The time series fragments of the first and second time series can bestored in a relational database. The fragments of the higher-level timeseries can comprise objects that inherit the characteristics of theobjects of the lower-level time series from which they are derived. Thefirst and second time series can comprise datasets of physiologic datapoints and the system can comprise a patient monitoring system whereinthe dynamic pattern of interaction comprises pathophysiologicdivergence.

As set forth below, data obtained from embodiments of the presentinvention may be employed to initiate or control a wide range ofactions, depending on the condition being identified and other designconsiderations. Examples of diagnostic activities that may be performedresponsive to data analysis performed by embodiments of the presentinvention include the identification of patterns indicative of airwayobstruction or instability, hypoventilation, hyperventilation andChenyne-Stokes respiration among others. Another exemplary use forembodiments of the present invention is to identify variations betweensimilar conditions, such as the difference between central andobstructive sleep apnea. Examples of therapeutic activities that may becontrolled or initiated responsive to data analysis performed inaccordance with embodiments of the present invention include providingan audiovisual alarm, waking a patient, providing a remote notification,sending human intervention, altering setting of life support event(ventilator), writing a severity index to a display device such as aDigicalc, switching display modes of a display device, showing a list ofoptions, printing a warning, performing genioglossal stimulation,performing phrenic nerve stimulation, performing diaphragm stimulation(implantable pacemaker), titrating a CPAP or bi-level pressure device,triggering another process, administering respiratory stimulant drugs,administering theophylline (caffeine or the like), reducing or ceasingadministration of narcotics, reducing administration of O₂ or closing acontrol loop to processes such as FiO₂, CPAP, PCA or PEEP. A number ofexamples of the application of embodiments of the present invention areset forth below.

In one exemplary embodiment of the present invention, the systemcomprises a monitor having a plurality of sensors for positioningadjacent a patient and a processor programmed to produce a first timedwaveform based on a first physiologic parameter of the patient, producea second timed waveform based on a second physiologic parameter which isgenerally subordinate to the first physiologic parameter, so that thesecond parameter normally changes in response to changes in the firstparameter, identify pathophysiologic divergence of at least one of thefirst and second physiologic parameters in relationship to the otherphysiologic parameter. The system can be further programmed to output anindication of the divergence, calculate an index of the divergenceand/or provide an indication based on the index. The first parametercan, for example, comprise an indication of the magnitude of timedventilation of a patient which can, for example, be the amplitude and/orfrequency of the variation in chest wall impedance and/or the amplitudeand/or frequency of the variation in nasal pressure and or the amplitudeand frequency of the variation of at least one of the tidal carbondioxide and/or the volume of ventilation or other measurable indicator.The second parameter can, for example, comprise a measure of oxygensaturation and can be pulse oximetry value or other measurable indicatorof arterial oxygenation such as a continuous or intermittent measurementof partial pressure of oxygen.

Another embodiment of the present invention may include a method ofmonitoring a patient comprising monitoring a patient to produce a firsttimed waveform of a first physiologic parameter and a second timedwaveform of a second physiologic parameter, the second physiologicparameter being physiologically subordinate to the first physiologicparameter, identifying a pattern indicative of divergence of at leastone of the waveforms in relation to a physiologically expected patternof the one of the other of the waveforms and outputting an indication ofthe divergence. The first timed waveform can be, for example defined bya time interval of greater than about 5-20 minutes. The first and secondtime series can, for example, be physiologic time series derived fromairflow and pulse oximetry. The processor can comprise a primaryprocessor, and the system can include a secondary processor and at leastone of a diagnostic and treatment device, the primary processor beingconnectable to the secondary processor, the secondary processor beingprogrammed to control at least one of the diagnostic and treatmentdevice, the secondary processor being programmed to respond to theoutput of said primary processor. The primary processor can beprogrammed to adjust the program of the secondary processor. Thetreatment device can be, for example an airflow delivery systemcontrolled by a secondary processor, the secondary processor beingprogrammed to recognize hypopneas, the primary processor adjusting theprogram of the secondary processor based on the identifying. In anotherembodiment, the treatment device can be an automatic defibrillator. Thesecondary processor can be mounted with at least one of the treatmentand diagnostic device, the primary processor being detachable from theconnection with the secondary processor. In one embodiment, the primaryprocessor is a hospital patient monitor adapted to monitor and analyze aplurality of different patient related signals, which may includeelectrocardiographic signals. The primary processor may comprise apolysomnography monitor capable of monitoring a plurality of differentsignals including encephalographic signals.

Embodiments of the present invention may comprise a monitor capable oforganizing the complexity of the actual operative dynamic interactionsof all of the signals both with respect to the absolute values, thedegree of relative variation, and rate of variation across along andacross multiple levels of the processed output and, more specifically,along and across multiple levels of multiple signals. Embodiments of thepresent invention may facilitate organization of interactive complexitydefining the physiologic outputs generated by the affected physiologicsystems, to recognize specific types and ranges of interactivepathophysiologic time series occurrences, and analysis of the componentsand evolution of such occurrences, thereby providing a timely outputthat reflects the true interactive, multi-system process impacting thepatient or to take automatic action base on the result of said analysis.

Embodiments of the present invention may provide an iterative processingsystem and method that analyzes both waveforms and timed laboratory datato produce an output corresponding to the dynamic evolution of theinteractive states of perturbation and compensation of physiologicsystems in real time. As a result, accurate information about thephysiologic state of the patient may be obtained.

Embodiments of the present invention may provide an iterative objectoriented waveform processing system, which can characterize, organize,and compare multiple signal levels across a plurality of signals bydividing each waveform level of each signal into objects fordiscretionary comparison within a relational database, object databaseor object-relational database. Embodiments of the present invention mayprovide a diagnostic system, which can convert conventionalhospital-based central telemetry and hard wired monitoring systems toprovide automatic processor based recognition of sleep apnea and airwayinstability. Such systems may be adapted to output data sets in asummary format so that this can be over read by a physician. In thismanner, maladies such as sleep apnea can be detected in a manner similarto that of other common diseases such as hypertension and diabetes.

Embodiments of the present invention may provide a diagnostic system,that can convert conventional hospital-based central telemetry and hardwired monitoring systems to provide processor based recognition ofmaladies such as sleep apnea and airway instability though therecognition of patterns of closely spaced apneas and/or hypopneas bothin real time and in overnight interpretive format.

Embodiments of the present invention may provide a system that isadapted to identify map, and link waveform clusters of apneas fromsimultaneously derived timed signals of multiple parameters that includechest wall impedance, pulse, airflow, exhaled carbon dioxide, systolictime intervals, oxygen saturation, EKG-ST segment level, or the like toenhance the real-time and overnight diagnosis of sleep apnea. Inaddition, embodiments of the present invention may be adapted to providetimely, real-time indication such as a warning or alarm of the presenceof apnea and/or hypopnea clusters so that nurses can be aware of thepresence of a potentially dangerous instability of the upper airwayduring titration of sedatives and/or narcotics.

Embodiments of the present invention may provide a system for therecognition of airway instability for combined cluster mapping of atimed dataset of parameters such as nasal oral pressure in conjunctionwith tidal CO₂ to identify clusters of conversion from nasal to oralbreathing and to optimally recognize clusters indicative of airwayinstability in association with tidal CO₂ measurement indicative ofhypoventilation.

An exemplary embodiment of the present invention may be employed toidentify pathophysiologic divergence of a plurality of physiologicallylinked parameters along a timed waveform over an extended period of timeto provide earlier warning or to provide reinforcement of thesignificance of a specific threshold breach. Exemplary embodiments ofthe present invention may be employed to identify an anomalous trend ofa first respiratory output in relation to a second respiratory outputwherein said first output is normally dependent on said second output toidentify divergence of said first respiratory output in relationship tothe expected trend said first respiratory output based on the trend ofsaid second output.

An exemplary embodiment of the present invention may be adapted to plotthe prolonged slope of a first respiratory output in relationship to theprolonged slope of a second respiratory output and to identifydivergence of said first respiratory output in relation to the slopesecond respiratory output. Further, exemplary embodiments of the presentinvention may be adapted to automatically trigger testing (andcomparison of the output) of a secondary intermittently testing monitorbased on the recognition of an adverse trend of the timed dataset outputof at least one continuously tested primary monitor.

Exemplary embodiments of the present invention may be adapted to providerecognition of lower airway obstruction (as with bronchospasm or chronicobstructive pulmonary disease) by exploiting the occurrence of theforced exhalation during the hyperventilation phase of recoveryintervals after and/or between intermittent upper airway obstruction toidentify obstructive flow patterns within the forced exhalation tracingand thereby identify lower airway obstruction superimposed on clusteredupper airway obstruction. Additionally, embodiments of the presentinvention may automatically customize treatment algorithms or diagnosticalgorithms based on the analysis of waveforms of the monitoredparameters. Finally, exemplary embodiments of the present invention mayinclude providing a method of linking a time series of expenseand/billing data to a time series of patient related outputs andexogenous actions applied to the patient so that the expense of eachaspect of the patients care can be correlated with both the proceduresand medications administered as well as the patient output both withrespect to dynamic patterns of interaction and specific laboratoryvalues or comparative results.

Embodiments of the present invention may comprise a digital objectprocessing system that functions to provide multidimensional waveformobject recognition both with respect to a single signal and multiplesignals. Such a system may be employed to identify and compare objects.Objects defined along one or more signals, including different signalsmay then be analyzed, identified and compared and defined by, and with,objects from different levels, if desired.

FIG. 1 a is a diagram of a three-dimensional cylindrical data matrix 1in accordance with embodiments of the present invention comprisingcorresponding, streaming, time series of objects from four differenttimed data sets. The cylindrical data matrix 1 shown in FIG. 1 aprovides a representation of a relational data processing structure ofmultiple time series. As this representation shows, a plurality of timeseries of objects are organized into different corresponding streams ofobjects, which can be conceptually represented as the cylindrical datamatrix 1, comprising processed, analyzed, and objectified data with timedefining the axis along the length of the cylindrical matrix 1. In thisexample, the cylindrical data matrix 1 is comprised of four time seriesstreams of processed objects, each stream having three levels. Each ofthe time series and their respective levels are matched and storedtogether in a relational database, object database or object-relationaldatabase. Each streaming time series of objects as from a single signalor source (e.g. airflow or oximetry, as in a matrix of physiologicsignals) is represented in the main cylinder 1 by a smaller cylinder (2,3, 4, 5) and each of these smaller cylinders is comprised of a groupingof ascending levels of time series of streaming objects (6, 7, 8) withthe higher levels being derived from the level below it. The streamingobjects in each ascending time series level are more complex with eachnew level, and these more complex objects contain the simpler objects ofthe lower levels as will be described.

FIG. 1 b shows a cross section 9 of the cylindrical data matrix 1 (FIG.1 a) curved back upon itself to illustrate an advantage of organizingthe data in this way. Each object from each grouping can be readilycompared and matched to other objects along the grouping and can furtherbe compared and matched to other objects from each other grouping.Furthermore, an object from one level of one signal at one time can bereadily compared to an object from another level of a different signalat a different time. The time series of streaming objects in FIG. 1 bare airflow, SPO₂, pulse, and a series of exogenous actions. This is atypical data structure, which would be used according to the presentinvention to monitor a patient at risk for sudden infant death syndromeand this will be discussed below in more detail.

Using this data structure, highly complex patterns and subtlerelationships between interactive and interdependent streams of objectscan be readily defined by searching the matched object streams. Thisallows for the recognition of the dynamic pattern interaction orconformation of the matrix of analyzed streaming interactive objects.

FIG. 2 a is a diagram of a three-dimensional representation ofcollective conformation of corresponding time series of objects of pulse(which can be heart rate and/or pulse amplitude or another pulse objectderived of one or more of the many pulse characteristics), oxygensaturation, airflow, chest wall movement, blood pressure, andinflammatory indicators during early infection, organized in accordancewith embodiments of the present invention. FIG. 2 b is a diagram of arepresentation of the dynamic multi-parameter conformation shown in FIG.2 a, but extended through the evolution of septic shock to the deathpoint. Each particular expected conformation will be defined by thespecific parameters chosen and the manner in which they are analyzed. Inan extension of the example a time series of expenditures would reflecta significant increase in the slope of resource (as financial or otherrecourses), which begins at a recognition point. If no recognition pointoccurs (i.e. the patient dies without the condition being diagnosed),the resource object time series may have a flat or even decreasingslope. The recognition of a specific dynamic pattern of interactionoccurrence falling within a specified range may be used to determine thepresence and severity of a specific of a biologic or physical process. Acorrelation with a time series of recourse allocation (such as timedexpenditures) and a time series of exogenous actions (such aspharmaceutical therapy or surgery) can be used to determine the cost andcauses of a given dynamic pattern of interaction and to better definethe efficacy of intervention. The conformation of FIGS. 2 a and 2 b canbe seen as comprising a progressive expansion, evolving to divergence ofthe parameters and eventual precipitous collapse and death. This can bereadily contrasted with the conformation of the cylindrical analyzeddata matrix 1 (FIG. 1 a) derived from the same analysis of the same timeseries grouping during the state of evolving airway instabilityassociated with excessive sequential or continuously infused dosing ofsedation or narcotics. In this case, the pattern is one of precipitous,cyclic, and convergent expansion and contraction with eventual terminalcontraction and death.

The following discussion presents an exemplary embodiment of the presentinvention for application to the patient care environment to achieveorganization and analysis of physiologic data and particularlyphysiologic signals and timed data sets of laboratory data from patientsduring a specific time period such as a hospitalization or perioperativeperiod.

The interaction of physiologic signals and laboratory data isparticularly complex, and requires a widely varied analysis to achievecomprehensive recognition of the many dynamic patterns of interactionindicative of potential life threatening pathophysiologic events. Thiswide variation is due, in part, to the remarkable variation in bothpatient and disease related factors. Such analysis is best performed inreal-time to provide timely intervention. To accomplish this level oforganization and DPI identification through multiple levels of each dataset or waveform and then across multiple levels of multiple data sets orwaveforms, the system processes and orders all of the datasets from eachsystem of the patient into a cylindrical matrix with each of the smallercylinders containing the levels in a specific ascending fashion. Anillustrative example of one exemplary method sequence for organizing thedata set of a single smaller cylinder (comprised of a single signal ofairflow) is shown in FIGS. 3 a-3 i.

According to this method, a processor executing instructions inaccordance with an embodiment of the present invention derives from atime series of raw data points (FIG. 3 a) a series of dipole objectswith their associated polarities and slopes (FIG. 3 b). As shown in FIG.3 c these dipoles can be represented as a slope set which removes thespatial attributes of the points and highlights relative change. Asshown in FIG. 3 c, various boundary types can be used to separate thedipoles into composite sequential objects and the figure shows threeillustrative boundary types: pattern limits, inflection points, andpolarity changes. As shown in FIG. 3 d, the system now has the criticalboundary points from which the wave pattern can be segmented and thecomposite objects can be derived and associated properties calculated.Although this is represented in FIG. 3 d as linear segments, eachcomposite object is actually comprised of the original set of dipoles sothat the user can choose to consider it a straight segment with oneslope or a curved segment defined by the entire slope set of thesegmented object. FIG. 3 e shows how the “trend” composite objects canbe identified to provide a simplified linear trend (or polarity)analysis.

Though the “trend” object set is useful as shown in FIG. 3 e, the timeseries can be segmented into other composite objects derived from theutilization of more or different user-defined boundary types. This canbe useful even if the curved shapes can be analyzed in the simpler trendanalysis because the selection of object boundaries at specific rangesor deflections helps to organize the objects as a direct function ofchanges in the physiologic output. In the example below, all threeboundary types are employed to derive a wave pattern wire frame. Thewire frame provides a simplified and very manageable view of the patternand has boundary attributes that can be vary useful in waveform patternsearching. This type of object segmentation can be shown (FIG. 3 f) as aset of object slopes with associated durations with the spatialrelationships removed. As is shown in FIG. 3 h this provides arepresentation for the manipulation by the user for object slope orduration deviation specification. Such deviations may be specifiedspecifically to individual segment objects or may be globallydesignated. Deviations may or may not be designated symmetrically.Multiple deviations can be specified per segment with scoring attributes(weighted deviations) to provide even more flexibility to the user tosearch for and correlate derived patterns. These two figures below shotsspecified deviations per segment (but not weighted deviations) for slopeand duration.

In the above exemplary manner, the time series can be organized with itsassociated objects and user-specified deviations, all of which arestored and categorized in a relational database, object database orobject-relational database. Also as will be discussed, once processed,portions of such a time series can then be applied as target searchobjects to other waveforms to search for similar objects and to scoretheir similarity.

FIG. 3 h is representative of the user selection of linear ranges ofvariations. Those skilled in the art will recognize that complex curvedshape variations can be specified in a similar way through the selectionof specific ranges in variations of the dipole slope data set (FIG. 3 c)defining the ranges of the curved target search object. It should benoted that, while the dipole set shown appears linearized, in fact, itcan be seen that the dipoles can contain all of the information in thedata points so that any curve present in the original raw data can bereproduced. It is cumbersome to input such ranges for each dipole sothis can be provided by specifying a curved shape and then moving apointer adjacent a curved shape to identify a range of shapes defining acurved target search object.

FIG. 4 is a graphical representation of an organization of the waveformsshown in FIGS. 3 a-3 h into ascending object levels in accordance withembodiments of the present invention. The graphs shown in FIG. 4illustrate the ascending object processing levels according toembodiments of the present invention, which are next applied to orderthe objects. These levels may be defined for each signal and comparisonscan be made across different levels between different signals. The firstlevel is comprised of the raw data set. The data from this first levelare then converted by the processor into a sequence of fundamentalobjects called dipoles to form the second (fundamental object) level. Inaccordance with embodiments of the present invention, these dipoleobjects, which will ultimately define complex multi-signal objects, arecomprised of these sequential fundamental objects having the simplecharacteristics of slope polarity, and duration. At this level, thedipoles can be processed to achieve a “best fit” dipole matching of twoor more signals (as will be discussed) and are used render the nextlevel, called the “composite object level.”

The composite object level is comprised of sequential and overlappingcomposite objects, which are composed of a specific sequence of slopedipoles as defined by selected search criteria. Each of these compositeobjects has similar primary characteristics of a slope duration, andpolarity to the fundamental objects. However, for the composite objects,the characteristic of slope can comprise a time series characteristicgiven as a slope dataset. The composite object level also has thecharacteristic of “intervening interval time-series” defined by a timeseries of the intervals between the recognized or selected compositeobjects. At this level, a wide range of discretionary indexcharacteristics can be derived from the comparison of basiccharacteristics of composite objects. Examples of such indexcharacteristics include: a “shape characteristic” as derived from anyspecified portion of the slope dataset of the object, a “positionalcharacteristic” as derived from, for example, the value of the lowest orhighest points of the object, or a “dimensional value characteristic” asderived by calculating the absolute difference between specified datapoints such as the value of the lowest and the highest values of theobject, or a “frequency characteristic” such as may be derived fromperforming a Fourier transform on the slope dataset of the object.

The next analysis level is called the “complex object level.” In thatlevel, each sequential complex object comprises plurality of compositeobjects meeting specific criteria. A complex object has the samecategories of primary characteristics and derived index characteristicsas a composite object. A complex object also has the additionalcharacteristics of “composite object frequency” or “composite objectorder” which can be used as search criteria defined by a selectedfrequency or order of composite object types, which are specified asdefining a given complex object. A complex object also has additionalhigher-level characteristics defined by the time-series of the shapes,dimensional values, and positional characteristics of its componentcomposite objects. As described for the composite objects, similar indexcharacteristics of the complex objects can be derived from thesecharacteristics for example; a “shape characteristic” derived from themean rate of change along the dataset of the mean slopes of compositeobjects. Alternatively characteristics or index characteristics may becombined with others. For example, a shape characteristic may becombined with a frequency characteristic to provide a time series of amathematical index of the slopes and the frequencies of the compositeobjects.

The next level, termed the “global objects level” is then derived fromthe time series of complex objects. At this level global characteristicsare derived from the time series datasets of complex objects (and all oftheir characteristics). At the global objects level, the processor canidentity specific patterns over many hours of time. An example of onespecific pattern which is readily recognizable at this level would be aregular monotonous frequency of occurrence of one substantially complexobject comprised of composite objects having alternating polarities,each with progressively rising or falling slope datasets. This patternis typical of Cheyene-Stokes Respirations and is distinctly differentfrom the pattern typical of upper airway instability at this globalobject level. Additional higher levels can be provided if desired as bya “comprehensive objects level” (not shown) which can include multipleovernight studies wherein a comprehensive object is comprised of adataset of “global objects.”

While FIG. 3 b and FIG. 4 illustrate the levels of object derivations ofa ventilation signal, in another example, a similar hierarchicalarchitecture can be derived for the timed data set of the pulse waveform(as from an arterial pressure monitor or a plethesmographic pulse). Herethe fundamental level is provided by the pulse tracing itself andincludes all the characteristics such as ascending and descending slope,amplitude, frequency or the like. This signal also includes thecharacteristic of pulse area (which, if applied to a precise signal suchas the flow plot through the descending aorta, is analogous to tidalvolume in the fundamental minute ventilation plot). When the pulsesignal is plethesmographic, it is analogous to a less precise signal ofventilation such as nasal pressure or thermister derived airflow. Withthese less precise measurements, because the absolute values are notreliable indicators of cardiac output or minute ventilation, the complexspatial relationships along and between signals become more importantthan any absolute value of components of the signal (such as absoluteamplitude of the ascending pulse or inspiration curve). In other words,the mathematical processing of multiple signals that are simply relatedto physiologic parameters (but are not a true measurement of thoseparameters) is best achieved by analyzing the complex spatialrelationships along and between those signals. To achieve this purpose,in accordance with embodiments of the present invention, as withventilation, the pulse signal is organized into a similar multi-levelhierarchy of overlapping time series of objects. Subsequently, these arecombined and compared with the processed objects of respiration toderive a unified object time series defined by multiple correspondingdata sets.

FIG. 5 a shows an exemplary pathophysiologic process associated with acharacteristic dynamic pattern of interaction. As discussed previously,this cyclic process is induced by upper airway instability. FIG. 5 bshows four corresponding signals derived from monitoring differentoutputs of the patient during a time interval wherein the dynamicprocess of FIG. 5 a is operative. The basic signals shown in FIG. 5 bare pulse, chest wall impedance, airflow, and oxygen saturation (SPO2).According to the present invention, these signals are processed intotime series fragments (as objects) and organized into the object levelsas previously discussed. For the purpose of organizing and analyzingcomplex interactions between these corresponding and/or simultaneouslyderived signals, similar ascending processes are applied to each signal.As shown in FIG. 5 c, these streaming objects, many of which overlap,project along a three-dimensional time series comprised of multiplelevels of a plurality of corresponding signals. A “multi-signal object”is comprised of at least one object from a first signal and at least oneobject from another signal. The multi-signal object shown in FIG. 5 chas the primary and index characteristics derived from each componentsignal and from the spatial, temporal, and frequency relationshipsbetween the component signals. As illustrated, the objects defining amulti-signal object can include those from analogous or non-analogouslevels. With this approach even complex and subtle dynamic patterns ofinteraction can be recognized.

This type of representation may be difficult to analyze in a clinicalenvironment, but is useful for the purpose of general representation ofthe data organization. At such a level of complexity, a completerepresentation of the time series does not lend itself well to atwo-dimensional graphical (and in some cases a three-dimensional)representation. Along the time series of sequential multi-signalobjects, the spatial characteristics of these multi-signal objectschange as a function of a plurality of interactive and differentcharacteristics derived from the different signals.

The mathematical power of this approach to characterize the achievedorganization of the complexity of the timed behavior of a physiologicsystem is illustrated by the application of this method to characterizethe codependent behavior of ventilation and arterial oxygen saturationand plethesmographic pulse. While these variables are codependent inthat a change in one variable generally causes a change in the othertwo, they are also each affected differently by different pathologicconditions and different preexisting pathologic changes. For example,the multi-signal objects comprising a time series of ventilation andarterial oxygen saturation and plethesmographic pulse in a sedated50-year-old obese smoker with asthma and sleep apnea are very differentthan those of a sleeping 50 year-old patient with Cheyene StokesRespiration and severe left ventricular dysfunction. These differencesare poorly organized or represented by any collection of two-dimensionalgraphical and/or mathematical representations. Despite this, throughoutthis disclosure, many of the signal interactions (such as those relatingto pathophysiologic divergence) will be discussed as a function of asimplified two-dimensional component representation for clarity based onolder standards of mathematical thought. However, it is one of theexpress purposes of the present invention to provide a mathematicallyrobust system for the organization and analysis of the complexmathematical interactions of biologic and other systems through theconstruction of time series sets of multidimensional and overlappingobjects.

To illustrate the complexity ordered by this approach, consider thecomponents of just one of the three simple recovery objects shown inFIGS. 5 b and 5 c. This single recovery object includes, by way ofexample, the exemplary characteristics, each of which may have clinicalrelevance when considered in relation to the timing and characteristicsof other objects, set forth in Table 1: TABLE 1 1. Amplitude, slope, andshape of the oxygen saturation rise event at the composite level 2.Amplitude, slope, and shape of the ventilation rise event at thecomposite level which contains the following characteristics at thefundamental level: a. Amplitude, slope, and shape of the inspirationrise object b. Amplitude, slope, and shape of the expiration fall objectc. Frequency and slope dataset of the breath to breath interval of tidalbreathing objects d. Frequency and slope data sets of the amplitude,slope, and shape of the pulse rise and fall events 3. Amplitude, slope,and shape of the pulse rise event at the composite level which containsthe following exemplary characteristics at the fundamental level a.Amplitude, slope, and shape of the plethesmographic pulse rise event b.Amplitude, slope, and shape of the plethesmographic pulse fall event c.Frequency and slope datasets of beat-to-beat interval of the pulse rated. Frequency and slope data set of the amplitude, slope, and shape ofthe pulse rise and fall events

As is readily apparent, it is not possible for a health care worker totimely evaluate the values or relationships of even a modest number ofthese parameters. For this reason, the development of an output based onthe analysis of these time series of objects to be presented in asuccinct and easily interpreted format is a desirable aspect of anembodiment of the present invention.

FIG. 6 shows several variations of a three-dimensional graphicalrepresentation of an output for clinical monitoring for enhancedrepresentation of the dependent and dynamic relationships betweenpatient variables. This representation may be referred to as a“monitoring cube.” These types of monitoring cubes may be adapted fordisplay on a hospital monitor, for example, for animation of thesummarized relationships between multiple interacting objects.

Such an animation can be shown as a small icon next to the real-timenumeric values typically displayed on present monitors. Once a baselineis established for a patient, either for example as the patient'sbaseline settings for a selected or steady state time period (of forexample 10-15 minutes) or by a selected or calculated set of normalranges, the cube may be illustrated as a square. For example, thepatient may initially have parameters out of the normal ranges and neverexhibit a square output. After the square for this patient isestablished, the cube is built from the evolving time series of theseparameters. A given region of the cube can be enlarged or reduced as theparticular value monitored increases or decreases respectively. Therelationship between these variables can be readily seen even if theyremain within the normal range. Moreover, a system adapted according toembodiments of the present invention may display distortions to theindividual constituent components of the square (see FIGS. 6 b-6 e) toillustrate the deviation of those particular constituent components frompredetermined normal ranges. The computer can flag with a red indicatora cube that is showing pathophysiologic divergence when compared withthe baseline values even though none of the values are at a typicalalarm threshold. If other abnormalities (such as the development ofpulse irregularity or a particular arrhythmia or ST segment change, thiscan be flagged on the cube so that the onset of these events can beconsidered in relation to other events. If preferred the time seriescomponents of the cube and their relationships to occurrences on othermonitored time series can be provided in a two-dimensional timeline.

Using this approach, time series relationships of multiple physiologicevents can be characterized on the screen with, for example, a smalldynamic animated icon in a succinct and easily understood way. There aremany other alternative ways to animate a summary of the dynamicrelationships and some of these will be discussed later in thedisclosure.

One of the longstanding problems associated with the comparison ofoutputs of multiple sensors to derive simultaneous multiple time seriesoutputs for the detection of pathophysiologic change is that theaccuracy and/or output of each sensor may be affected by differentphysiologic mechanisms in different ways. Because of this, the value ofmatching an absolute value of one measurement to an absolute value ofanother measurement is degraded. This is particularly true if themeasurement technique or either of the values is imprecise. For example,when minute ventilation is measured by a precise method such as apneumotachometer, then the relationship between the absolute values ofthe minute ventilation and the oxygen saturation are particularlyrelevant. However, if minute ventilation is being trended as by nasalthermister or nasal pressure monitoring or by chest wall impedance thenthe absolute values become much less useful. However, according to oneaspect of embodiments of the present invention, the application of theslope dipole method, the relationship between a plurality ofsimultaneously derived signals can be determined independent of therelationships of the absolute values of the signals. In this way,simultaneously derived signals can be identified as having convergenceconsistent with physiologic subordination or divergent shapes consistentwith the development of a pathologic relationship or inaccurate dataacquisition.

As noted, with physiologically linked signals, a specific occurrence ormagnitude of change in one signal in relationship to such a change inanother signal may be more important and much more reproducible than theabsolute value relationships of the respective signals. For this reason,the slope dipole method provides an important advantage to integrate andanalyze such signals. Using this signal integration method, twosimultaneously acquired physiologic linked signals are compared by aprocessor over corresponding intervals by matching the respective slopedipoles between the signals. Although the exact delay between thesignals may not be known, the processor can identity this by identifyingthe best match between the dipole sets. Embodiments of the presentinvention may consider this to be a “best match” constrained by presetlimits. For example, with respect to ventilation and oximetry, a presetlimit could be provided in the range of 10-40 seconds although otherlimits could be used depending on the hardware, probe site andaveraging, intervals chosen. After the best match is identified, therelationships between the signals are compared (for example, theprocessor can compare the slope dipole time series of oxygen saturationto the slope dipole time series of an index of the magnitude ofventilation). In this preferred embodiment, each slope dipole iscompared. It is considered preferable that the dipoles of eachrespective parameter relate to a similar duration (for example. 1-4seconds). With respect to airflow, calculation of the magnitude value ofairflow may require sampling at a frequency of 25 hertz or higher,however, the sampling frequency of the secondary plot of the magnitudevalue of the index can, for example, be averaged in a range of one hertzto match the averaging interval of the data set of oxygen saturation.Once the signals have been sufficiently matched at the dipole level,they can be further matched at the composite level. In accordance withembodiments of the present invention, most object matching acrossdifferent signals is performed at the fundamental level or higher,however timing matching can be performed at the dipole level and thiscan be combined with higher level matching to optimize a timing match.

FIGS. 9, 10, and 11, show schematic mapping of matched clusters ofairway instability (of the type shown in FIG. 5 b) where clusters arerecognized and their components matched at the composite object level.When the objects are matched, the baseline range relationship betweenthe signals can be determined. This baseline range relationship can be amagnitude value relationship or a slope relationship. The signals canthen be monitored for variance from this baseline range, which canindicate pathology or signal inaccurate. The variance from baseline canbe, for example, an increase in the relative value of ventilation inrelation to the oximetry value or a greater rate of fall in oxygensaturation in relation to the duration and/or slope of fall ofventilation. In another example, the variance can include a change fromthe baseline delay between delta points along the signals.

With multiple processed signals as defined above, the user, who can bethe program developer, can then follow the following to complete theprocess of searching for a specific pattern of relationships between thesignals:

-   -   1. Specify a Search Wave Pattern    -   2. Analyze and divide the search pattern into objects    -   3. Input the allowed deviation (if any) from the search pattern        or the objects comprising it.    -   4 Input additional required relationships (if any) to other        objects in the target waveform.    -   5. Apply the search pattern or selected component objects        thereof to a target waveform.

Various methods of identification may be employed to provide a wavepattern to the system. For example, users may:

-   -   1. Choose from a menu of pattern options.    -   2. Select dimensional ranges for sequential related patterns of        ascending complexity.    -   3. Draw a wave pattern within the system with a pointing or pen        device.    -   4. Provide a scanned waveform.    -   5. Provide a data feed from another system.    -   6. Describe the pattern in natural language.    -   7. Type in a set of points.    -   8. Highlight a sub-section of another waveform within the        system.

In accordance with embodiments of the present invention, the system canbe automated such that search is automatically applied once the criteriaare established. Also, the method of identification of the searchpattern can be preset. For example, the occurrence of a specificsequence of objects can be used as a trigger to select a region (whichcan be an object of the types previously described) as the specifiedsearch pattern, the processor can automatically search for other suchpatterns in the rest of the study. The result of any of these inputswould be a set of points with or without a reference coordinate systemdefinition as shown in FIGS. 3 a-3 h.

After receiving search criteria, the system begins its analysis of thetarget set of points to derive a series of object sets. These sets willbe used to identify key properties of the wave pattern. These objects(and their boundaries) will provide a set of attributes which are mostlikely to be significant in the wave pattern and that can be acted uponin the following ways:

-   -   1. To provide parameters on which sets of rules may be applied        for the identification of expected conditions.    -   2. To provide parameters that can be associated with        specifically allowable deviations and/or a globally applied        deviation.    -   3. To provide parameters than can be used to score the relative        similarity of patterns within the target waveform.

In such a manner, a search can be carried out for specificpathophysiologic anomalies. This can be carried out routinely by thesoftware or on demand.

One example of the clinical utility of the application of the objectprocessing and recognition system to physiologic signals is provided byidentification of upper airway instability. As discussed in theaforementioned patents and application, events associated with airwayinstability are precipitous. In particular, the airway closure isprecipitous and results in a rapid fall in ventilation and oxygensaturation. Also the subsequent airway opening airway is precipitous,and because ventilation drive has risen during closure the resultingventilation flow rate (as represented by a measurement of airflowdeflection amplitude) rises rapidly associated with recovery. Also,after the period of high flow rate associated with the recovery the flowrate precipitously declines when the chemoreceptors of the brain senseventilation overshoot. In this way, along a single tracing of timedairflow deflection amplitude, three predictable precipitous relativelylinear and unidirectional waveform deflections changes have occurred ina particular sequence in a manner analogous to the tracing of the SpO₂or pulse rate. Subsequent to this, the unstable airway closes suddenlypropagating the cluster of cycles in all of these waveforms.

As noted above, a hallmark of airway instability is a particular clustertimed sequence of precipitous, unidirectional changes in the timed dataset. For this reason, the first composite object to be recognized isdefined by a precipitous unidirectional change in timed output of one ofthe above parameters. The system then recognizes along the fundamentalsequential unipolar composite objects and builds the composite levelcomprised of time series of these composite objects. One presentlypreferred embodiment uses the following method to accomplish this task.A unipolar “decline object” is a set of consecutive points over whichthe parameter level of the patient is substantially continually falling.A unipolar “rise object” is a set of consecutive points over which theparameter is substantially continually increasing. A “negative pattern”is a decline together with a rise object wherein the rise follows thedecline within a predetermined interval. A “positive pattern” is a risetogether with a decline wherein the decline follows the rise within apredetermined interval. How closely these composite objects can followeach other is a specifiable parameter. At the complex object level, acluster is a set of consecutive positive or negative patterns thatappear close together. How closely these patterns must follow each otherto qualify, as a cluster is a specifiable parameter.

In operation, a system constructed in accordance with embodiments of thepresent invention may proceed in several phases. As an example, in afirst phase, decline and rise objects are identified. In a second phase,negative and positive patterns are identified. In a third phase,clusters of negative and/or positive patterns are identified. In afourth phase, a relationship between the events and patterns iscalculated and outputted. In a fifth phase, a diagnosis and severityindexing of airway or ventilation instability or sleep/sedation apnea ismade. In a sixth phase, a textual alarm or signal is outputted and/ortreatment is automatically modified to eliminate cluster. The processmay then be repeated with each addition to the dataset in real-time orwith stored timed datasets.

Embodiments of the present invention may apply either a linear oriterative dipole slope approach to the recognition of waveform events.Since the events associated with airway collapse and recovery aregenerally precipitous and unipolar, the linear method suffices for therecognition and characterization of these nonlinear waves. However, theiterative dipole slope approach is particularly versatile and ispreferred in situations wherein the user would like an option to selectthe automatically identification of a specific range of nonlinear ormore complex waves. Using the iterative dipole slope method, the usercan select specific consecutive sets of points from reference casesalong a waveform as by sliding the pointer over a specific waveformregion. Alternatively, the user can draw the desired target waveform ona scaled grid. The user can also input or draw range limits therebyspecifying an object or set of objects for the microprocessor torecognize along the remainder of the waveform or along other waveforms.Alternatively, the processor can automatically select a set of objectsbased on pre-selected criteria (as will be discussed). Since theiterative dipole process output is shape-dependent (including frequencyand amplitude) but is not necessarily point dependent, it is highlysuited to function as a versatile and discretionary engine forperforming waveform pattern searches. In accordance with embodiments ofthe present invention, the waveform can be searched by selecting andapplying objects to function as Boolean operators to search a waveform.The user can specify whether these objects are required in the sameorder. Recognized object sequences along the waveform can be scored tochoose the degree of match with the selected range. If desired, (as forresearch analysis of waveform behavior) anomalies within objects oroccurring in one or more of a plurality of simultaneously processedtracings can be identified and stored for analysis.

For the purpose of mathematically defining the presently preferredobject system, according to the present invention, for recognition ofdigital object patterns let o₁, o₂, . . . , o_(m) be original datapoints. The data can be converted to a smoother data set, x₁, x₂, . . ., x_(n), by using a moving n average of the data points as a 1-4 secondaverage for cluster recognition or as a 15-30 second average for theidentification of a pathophysiologic divergence. For the sake of clarityof presentation, assume that x, is the average of the original datapoints for the i^(th) second. A dipole is defined to be a pair ofconsecutive data points. Let d_(i)=(x_(i), x_(i+1)) be the i^(th)dipole, for i=1, 2, . . . , n−1. The polarity, say p_(i) of the i^(th)dipole is the sign of x_(i+1)−x_(i), (i.e. p_(i)=1 if x_(i+1)>x_(i),p_(i)=0 if x_(i+1)=x_(i), and p_(i)=−1 if x_(i+1)<x_(i)). For thepurpose of automatic recognition of user specified, more complexnonlinear waveforms, the data can be converted to a set of dipoleslopes, z₁, z₂, . . . , z_(n). Let z_(i)=(x_(i+1)−x_(i),) be the i^(th)dipole slope, for i=1, 2, . . . , n−1.

As an exemplary way to recognize a decline event by applying theiterative slope dipole method in accordance with embodiments of thepresent invention, let, {z₁, z₂, . . . , z_(n)} be a set of consecutivedipole slopes. Then {z₁, z₂, . . . , z_(n)} is a decline if it satisfiesthe following conditions:

-   -   1. z₁, z₂, . . . , z_(n) are less than zero i.e., the parameter        level of the patient is continually falling over the set of        dipole slopes. This condition may be partially relaxed to adjust        for outliers, as by the method described below for the linear        method.    -   2. The relationship of z₁ to z₂, z₂ to z₃, . . . z_(n−1) to        z_(n) is/are specified parameter(s) defining the shape of the        decline object, these specified parameters can be derived from        the processor based calculations of the dipole slopes made from        a user selected consecutive data set or from a set drawn by the        user onto a scaled grid.

To recognize a rise event a similar method is applied wherein z₁, z₂, .. . , z_(n) are greater than zero. Complex events, which include riseand fall components are built from these more composite objects.Alternatively, a specific magnitude of change along a dipole slopedataset can be used to specify a complex object comprised of twocomposite objects separating at the point of change (a waveformdeflection point). In one application the user slides the cursor overthe portion of the wave, which is to be selected, and this region ishighlighted and enlarged and analyzed with respect to the presence ofmore composite objects. The dimensions of the object and the slope dataset, which defines it, can be displayed next to the enlarged waveform.If the object is complex (as having a plurality of segments of differingslope polarity or having regions wherein the slope rapidly changes as bya selectable threshold) then each composite object is displayedseparately with the respective dimensions and slope data sets. In thisway the operator can confirm that this is the actual configurationdesired and the user is provided with a summary of the spatial anddimensional characteristics of the composite objects, which define theactual selected region. The operator can select a range of variations ofthe slope data set or chance the way in which the composite objects aredefined, as by modifying the threshold for a sustained change in slopevalue along the slope dataset. (For example, by allotting at least oneportion of the slopes to vary by a specified amount, such as 10%, byinputting graphically the variations allowed. If the operator “OKs” thisselection, the processor searches the entire timed dataset for thecomposite objects, building the selected object from the compositeobjects if identified

To recognize a decline event by applying the linear method according tothe present invention, let {x_(i), x_(i+1), . . . , x_(r)} be a set ofconsecutive points and let s=(x_(r)−x_(d)/(r−i) be the overall slope ofthese points. Although the slope could be defined by using linearregression or the like, the previous definition allows for improvedfidelity of the output by allotting rejection based on outlieridentification. Then {x_(i), x_(i+1), . . . x_(r)} is a decline if itsatisfies the following conditions:

-   -   1. x_(i)>x_(i+1)2> . . . x_(r), i.e. the parameter level of the        patient is continually falling over the set of points. This        condition may be partially relaxed to adjust for outliers, as        described belong.    -   2. r−i≧D_(min), where D_(min) is a specified parameter that        controls the minimum duration of a decline.    -   3. s_(min)≦s≦s_(max), where s_(min) and s_(max) are parameters        that specify the minimum and maximum slope of a decline,        respectively.

The set {97, 95, 94, 96, 92, 91, 90, 88}, does not satisfy the currentdefinition of a decline even though the overall level of the parameteris clearly falling during this interval. The fourth data point, 96, isan outlier to the overall pattern. In order to recognize this intervalas a decline, the first condition must be relaxed to ignore outliers.The modified condition 1 is:

1. *Condition 1 with Outlier Detection

-   -   a. i>xi+1,    -   b. xi>xi+1 or xi+1>xj+2 for j=i+1, . . . , r−2.    -   c. xr−1>x_(r).

To recognize a rise event, let {x_(i), x_(i+1), . . . , x_(r)} be a setof consecutive points and let s=(x_(r)−x_(i)/(r−i) be the overall slopeof these points. Then {x_(i), x_(i+1), . . . , x_(r)} is a rise if itsatisfies the following conditions:

-   -   1. x_(i)<x_(i+1)< . . . <x_(r), i.e., the parameter level of the        patient is continually rising over the set of points. This        condition may be partially relaxed to adjust for outliers, as        described below.    -   2. r−i≧D_(min), where D_(min) is a specified parameter that        controls the minimum duration of rise.    -   3. s_(min)≦s≦s_(max), (where s_(min) and s_(max) are parameters        that specify the minimum and maximum slope of a decline,        respectively.

Similar to declines, the first condition of the definition of a rise isrelaxed in order to ignore outliers. The modified condition 1 is:

Condition 1 with Outlier Detection

a. xi<xi+1.

b. xj<xj+1 or xj+1<xj+2 for j=i+1, . . . , r−2.

c. xr−1<xr.

To recognize a negative pattern the program, iterates through the dataand recognize events and then identifies event relationships to definethe patterns. The system uses polarities (as defined by the direction ofparameter movement in a positive or negative direction) to test forcondition (1*) rather than testing for greater than or less than. Thissimplifies the computer code by permitting the recognition of alldecline and rise events to be combined in a single routine and ensuresthat decline events and rise events do not overlap, except that they mayshare an endpoint. The tables below show how condition (1*) can beimplemented using polarities. Equivalent Condition 1* For Decline EventCondition 1* Equivalent Condition a. x_(i) > x_(i−1) p_(i) = −1 b.x_(i) > x_(j−1) or x_(j−1) > x_(j−2) P₁ = −1 or P_(j+1) = −1 c.x_(r−1) > x_(r) P_(r−1) = −1

Equivalent Condition 1* For Rise Event Condition 1* Equivalent Conditiona. x_(i) > x_(i 1) p_(i) = 1 b. x_(i) > x_(j−1) or x_(j−1) > x_(j−2) P₁= 1 or P_(j+1) = 1 c. x_(r−1) > x_(r) P_(r−1) = 1

Exemplary pseudocode for a combined microprocessor method, whichrecognizes both unipolar decline events and unipolar rise events, isshown below. In this exemplary code, E is the set of events found by themethod, where each event is either a decline or a rise. EVENTRECOGNITION i = 1 Exent_polarity = p₁ for j = 2 to n−2 if (p_(i) .≠event_polarity) and (p_(i+1) . ≠ event_polarity) r = j X =¦x_(p).....x_(l)¦ if event_polarity = 1 Add X to E if it satisfies riseconditions (2) and (3) elseif event_polarity = −1 Add X to E if itsatisfies decline conditions (2) and (3) endif i = j event_polarity =p_(i) Endif

endfor

Add X={x_(i), . . . , x_(n)} to E if it satisfies either the rise ordecline conditions

Next, A specific pattern is recognized by identifying a certain sequenceof consecutive events, as defined above, which comply with specificspatial relationships. For example, a negative pattern is recognizedwhen a decline event, say D={x_(i), . . . , x_(j)}, together with a riseevent, say R={x_(k), . . . , x_(m)}, that closely follows it. Inparticular, D and R must satisfy k−i≦t_(dr), where t_(dr) is aparameter, specified by the user, that controls the maximum amount oftime between D and R to qualify as a negative pattern.

The exemplary pseudocode for the microprocessor system to recognize anegative pattern is shown below. Let E={E₁, E₂, . . . , E_(q)} be theset of events (decline events and rise events) found by the eventrecognition method, and let DR be the set of a negative pattern.NEGATIVE PATTERN RECOGNITION for h = 1 to q−1 Let D = {x_(i),...,x_(j),} be the event E_(h) if D is a decline event Let R ={x_(k),...,X_(m),} be the event E_(h+1) if R is a rise event gap = k − jif gap ≦ t_(dr) Add (D,R) to the list of negative patterns endif endifendif endfor

As noted, a cluster is a set of consecutive negative or positivepatterns that appear close together. In particular, let C={DR_(i),DR_(i+1), . . . , DR_(k)} be a set of consecutive negative patterns.s.sub.j be the time at which DR_(j) starts, and e_(j) be the time atwhich DR_(j) ends. Then C is a cluster if it satisfies the followingconditions:

-   -   1. s_(j+1)−e_(j)≦t_(c), for j=i, . . . , k−1, where t_(c) is a        parameter, specified by the user, that controls the maximum        amount of time between consecutive negative patterns in a        cluster.    -   2. k−i−1≦c_(min), where e_(min) is a parameter, specified by the        user, that controls the minimum number of negative patterns in a        cluster.

The pseudocode for the algorithm to recognize clusters of negativepatterns is shown below. Let DR={DR₁, DR₂, . . . , DR_(r)} be the set ofnegative patterns found by the above pattern recognition method. CLUSTERRECOGNITION ( OF NEGATIVE PATTERNS) f = 1: for h = 2:r Let R =¦x_(l),...,X_(m),¦ be the rise in DR_(h−1) Let D = ¦x_(l),...,X_(j),¦ bethe in decline in DR_(h) gap = i − m if gap > t_(c) g = h −1 if g − f +1≧ c_(min) Add DR_(l),..., Dr_(i l),..., DR_(g) to the list of clustersendif f = h endif endfor g = r if g − f − 1 ≧ c_(min) Add DR_(i−Dri−1).. . . . DR_(g) to the list of clusters Endif

In accordance with embodiments of the present invention, this objectbased linear method maps the unique events, patterns and clustersassociated with airway instability because the sequential waveformevents associated with airway closure and reopening are each both rapid,substantially unipolar and relatively linear. Also the patterns andclusters derived are spatially predictable since these precipitousphysiologic changes are predictably subject to rapid reversal by thephysiologic control system, which is attempting to maintain tightcontrol of the baseline range. Because timed data sets with predictablesequences of precipitous unidirectional deflections occur across a widerange of parameters, the same digital pattern recognition methods can beapplied across a wide range of clustering outputs, which are derivedfrom airway instability. Indeed, the basic underlying mechanismproducing each respective cluster is substantially the same (e.g.clusters of positive pulse rate deflections or positive airflowamplitude deflections). For this reason, this same system and method canbe applied to a timed data set of the oxygen saturation, pulse rate (asfor example determined by a beat to beat calculation), amplitude of thedeflection of the chest wall impedance waveform per breath, amplitude ofdeflection of the airflow signal per breath (or other correlated ofminute ventilation), systolic time intervals, blood pressure, deflectionamplitude of the nasal pressure, the maximum exhaled CO₂ per breath, andother signals. Additional details of the application of this digitalpattern recognition method to identify clusters are provided in patentapplication Ser. No. 09/409,264, which is assigned to the presentinventors.

Next, for the purpose of building the multi-signal object, a pluralityof physiologically linked signals are analyzed for the purpose ofrecognizing corresponding patterns and corresponding physiologicconvergence for the optimal identification of the cluster cycles. Forexample, a primary signal such as airflow is analyzed along with acontemporaneously measured secondary signal such as oxygen saturation asby the method and system discussed previously. As discussed previously,for the purpose of organizing the data set and simplifying the analysis,the raw airflow signal is processed to a composite object level. Forexample, the composite level of airflow can be a data set of theamplitude and/or frequency of the tidal airflow as by thermister orpressure sensor, or another plot, which is indicative of the generalmagnitude of the timed tidal airflow. In an exemplary embodiment, amathematical index (such as the product) of the frequency and amplitudeis preferred, because such an index takes into account the importantattenuation of both amplitude and frequency during obstructivebreathing. Furthermore, both the frequency and amplitude are oftenmarkedly increased during the recovery interval between apneas andhypopneas. It is not necessary that such a plot reflect exactly the truevalue of the minute ventilation but rather, it is important that theplot reflect the degree of change of a given level of minuteventilation. Since these two signals are physiologically linked, anabrupt change in the primary signal (airflow) generally will producereadily identifiable change in the subordinate signal (oxygensaturation). As previously noted, since the events which are associatedwith airway collapse are precipitous, the onset of these precipitousevents represent a brief period of rapid change which allows for optimaldetection of the linkage between the primary signal and the subordinatesignal.

The signals can be time matched by dipole slopes at the fundamentallevel. In addition, in one exemplary embodiment of the presentinvention, the point of onset of precipitous change is identified at thecomposite object level of the primary signal and this is linked to acorresponding point of a precipitous change in the composite objectlevel of the subordinate signal. This condition is referred to herein asa “delta point.” As shown in FIGS. 9, 10, and 11, a first delta point isidentified in the primary signal and in this example is defined by theonset of a rise object. A corresponding first delta point is identifiedin the subordinate signal and this corresponds to the onset of a riseobject in the subordinate signal. A second delta point is identifiedwhich is defined by the point of onset of a fall object in the primarysignal and which corresponds to a second delta point in the subordinatesignal defined by the onset of a fall event in the secondary signal. Thepoint preceding the second delta point (the “hyperventilation referencepoint”) is considered a reference indicating an output associated with adegree of ventilation, which substantially exceeds normal ventilationand normally is at least twice normal ventilation. When applying airflowas the primary signal and oximetry as the subordinate signal, the firstdelta point match is the most precise point match along the twointegrated waveforms and therefore comprises a (“timing referencepoint”) for optimally adjusting for any delay between the correspondingobjects of the two or more signals. The mathematical aggregate (such asthe mean) of an index of the duration and slope, and/or frequencies ofcomposite rise and fall objects of the fundamental level of tidalventilation along a short region adjacent these reference points can beapplied as a general reference for comparison to define the presence ofrelative levels of ventilation within objects along other portions ofthe airflow time series. Important fundamental object characteristics atthese reference points are the slope and duration of the rise object orfall object because these are related to volume of air, which was movedduring the tidal breath. The fundamental objects comprising the tidalbreaths at the reference hyperventilation point along the compositelevel are expected to have a high slope (absolute value) and a highfrequency. In this way, both high and low reference ranges aredetermined for the signal. In another exemplary embodiment, these pointscan be used to identify the spatial shape configuration of the rise andfall objects at the fundamental level during the rise and fall objectsat the composite level.

As shown in FIGS. 9 and 10, using this method at the composite objectlevel, a first object (FIG. 11) can then be identified in the primarysignal between the first delta point and the second delta point which isdesignated a recovery object. As also shown in FIG. 11, the matchedrecovery object is also identified in the subordinate signal as thepoint of onset of the rise object to the point of the onset of the nextsubsequent fall object. In an exemplary embodiment, the recovery objectis preceded by the apnea/hypopnea object which is defined by the pointof onset of the fall object to the point of onset of the next riseobject in both the primary and subordinate signals.

As shown in FIG. 12, a recovery object recognized at the composite levelcan used to specify a region for comparison of sequential objects at thefundamental object level. Here, upon recognition of the presence of arecovery object (where it is anticipated that the ventilation effortwill be high) the ratio of the slope of exhalation objects to the slopeof inhalation objects can be compared within the recovery object and thetime series derived from these comparisons can be plotted if desired.During upper airway obstruction, the inspiration is slowed to a greaterdegree than exhalation. The magnitude change of the ratio during theclusters of apneas provides an index of the magnitude of upper airwaynarrowing (which selectively slows inhalation during the clusteredapnea/hypopnea objects). However, during the recovery object or at the“hyperventilation reference point”, the upper airway should be wide openfor both inhalation and exhalation and this can be used as a referencebecause, during this time. The absolute slope of the fundamental objectsduring recovery can then be compared to the absolute slope of thefundamental objects during other times along the night to provide anindication of upper or looser airway narrowing.

When airflow is the primary signal and oximetry the subordinate, themost reliable delta point is the point of onset of a rapid rise inventilation (in a patient with an oxygen saturation, at the point ofonset point, of less than 96-97%). Patients with very unstable airwayswill generally have relatively short recovery objects. Other patientswith more stable airways may have a multi-phasic slope of decline inairflow during the recovery objects herein, for example, there is aninitial precipitous decline event in the airflow parameter and then aplateau or a much more slight decline which can be followed by a secondprecipitous decline to virtual absence of ventilation. Using the slopedipole method these composite objects can be readily separated such thatthe occurrence of multiple composite objects (especially wherein theslopes are close to zero) or a single object with a prolonged slowlyfalling slope dataset occurring immediately after the first data point,can be identified. These patients generally have longer recoveryintervals and more stable airways. The identification of a declineobject associated with decline from the hyperventilation phase ofrecovery followed by a plateau and/or a second decline object associatedwith the onset of apnea is useful to indicate the presence of a greaterdegree of airway stability. Accordingly, with the airflow signal, athird delta point (FIG. 12) designated a “airflow deflection point” canoften be identified in the airflow tracing corresponding to thedeflection point at the nadir of drop in airflow at the end of therecovery. This point is often less definable than the second delta pointand for this reason matching the second delta points in the airflow andoximetry signals is preferred although with some tracings a matchbetween the airflow deflection point and the second delta point in theoximetry dataset provides a better match.

If a significant decline in airflow is identified after the “airflowdeflection point” then the region of the intervening decline object andthe next delta point (onset of the next recovery) is designated areference “ventilation nadir region”. If the region or object(s) fromthe second delta point to ventilation deflection point is very short (as0-3 breaths) and the ventilation nadir region has a mean slope close toor equal to zero (i.e. the region is relatively flat) and the deflectionamplitude is close to zero or otherwise very small indicating now orvery little ventilation, then the airway is designated as highlyunstable.

Another example of object processing at the fundamental object level,according to the present invention, includes the processor-basedidentification of fluttering of the plateau on the pressure signal torecognize partial upper airway obstruction. During the nasal pressuremonitoring a fluttering plateau associated with obstructive breathingoften occurs intervening a rise event and a fall event of tidalbreathing. Since the plateau objects are easily recognizable at thefundamental level and readily separated using the present objectrecognition system the plateau can be processed for the tiny rise andfall objects associated with fluttering and the frequency of theseobjects can be determined. Alternatively, a Fourier transform can beapplied to the plateau objects between the rise and fall events of thenasal pressure signal to recognize the presence of fluttering or anothermethod can be utilized which provides an index of the degree offluttering of the plateau objects.

Since reduced effort also lowers the slope of exhalation andinspiration, the configuration (as defined by the slope dataset of thedipoles defining the fundamental objects of both inspiration andexpiration at the reference objects) can be applied as referencefundamental object configurations defining the presence ofhyperventilation or hypopnea. This process is similar to the selectionprocess for identifying search objects described earlier but in thiscase the input region is pre-selected. In an example, the range ofcharacteristics of the objects at the fundamental level derived from oneor more tidal breaths occurring prior to the second airflow delta pointcan be used to designate a reference hyperventilation objects range.Alternatively, the object-based characteristics, defined by of the rangeof characteristics of the objects derived from one or more tidal breathsoccurring prior to the first airflow delta point can be used designate areference hypopnea objects range. The processor can then automaticallyassess object ranges along other points of the tracing. In this way, theprocessor can apply an artificial intelligence process to theidentification of hypopneas by the following process:

-   -   1. Identify the region wherein a hypopnea is expected (as for        example two to three tidal breaths prior to the first airflow        delta point).    -   2. Select this as a region for objects processing to define the        characteristics of hypopneas in this patient.    -   3. Process the region using the slope dipole method to define        the range of fundamental objects comprising the target region.    -   4. Compare the identified range of objects to other analogous        objects along to tracing to identify new objects having similar        characteristics.    -   5. Using the criteria derived from the objects defining the        target region search the processed waveform for other regions        having matching sequences of new objects and identify those        regions.    -   6. Provide an output based on said identification and/or take        action (e.g. increase CPAP) based on said identification.

These processing methods exploit the recognition that certain regionsalong a multi-signal object (as within a cluster) have a very highprobability of association with certain levels of ventilation. Theobjects defining those regions can then be used as a reference or as anopportunity to examine for the effects of a given level of ventilationeffort on the flow characteristics. Patients with obstructive sleepapnea will have a fall in the slopes of fundamental inspiration objectsduring decline objects at the composite level indicative of upper airwayocclusion. Also, as shown in FIG. 12, patients with asthma or chronicobstructive lung disease will have a reduced slope of the exhalationwhen compared to the slope of inhalation during the rise objects betweenapneas at the base level. According to one embodiment of the presentinvention, the time series of the ratio of the slope of inhalationobjects to exhalation objects is included with the basic time series.Patients with simple, uncomplicated obstructive apnea will have clustersof increasing slope ratios with the ratio rising to about one during therecovery objects. Patients with combined obstructive apnea and asthma orchronic obstructive lung disease will have a greater rise in sloperatios during the recovery objects to into the range of 2-3 or greater,indicating the development of obstructive lower airways during the rapidbreathing associated with recovery.

A system for processing, analyzing and acting on a time series ofmulti-signal objects in accordance with one embodiment of the presentinvention is shown in FIG. 8. The examples provided herein show theapplication of this system for real time detection, monitoring, andtreatment of upper airway and ventilation instability and for the timelyidentification of pathophysiologic divergence. The system includes aportable bedside processor 10, which may comprise a microprocessor,having at least a first sensor 20 and a second sensor 25, which mayprovide input for at least two of the signals discussed above. Thesystem includes a transmitter 35 to a central processing unit 37. Thebedside processor 10 may include an output screen 38, which provides thenurse with a bedside indication of the sensor output. The bedsideprocessor 10 can be connected to a controller of a treatment orstimulation device 50 (which can include, for example, a positivepressure delivery device, an automatic defibrillator, a vibrator orother tactile stimulator, a drug delivery system such as a syringe pumpor back to the processor to adjust the analysis of the time-seriesinputs), the central unit 37 preferably includes an output screen 55 andprinter 60 for generating a hard copy for physician interpretation. Inaccordance with embodiments of the present invention, the system allowsrecognition of conditions such as airway instability, complicationsrelated to such instability, and pathophysiologic divergence in realtime from a single or multiple inputs. Moreover, embodiments of thepresent invention may be programmed or otherwise adapted to identifyrecurring patterns in a wide range of signals to identify conditionsassociated with those recurring patterns. In the embodiment illustratedin FIG. 8, the bedside processor 10 is connected to a secondaryprocessor 40 which can be a separate unit. The secondary processor 40may be adapted to perform measurements intermittently and/or on demand.Examples of measurements that may be performed include non-invasiveblood pressure monitoring or monitoring with an ex-vivo monitor, whichdraws blood into contact with a sensor on demand for testing to derivedata points for addition to the multi-signal objects. The secondaryprocessor 40 includes at least one sensor 45. The output of the bedsideprocessor can be transmitted, for example, to a central processor 37which may comprise a hospital monitoring station, or to the bedsidemonitor 10 to render a new object output, action, or analysis. In anexemplary embodiment of the present invention, the method of hypopnearecognition discussed previously can be coupled with a treatment device50 such as a CPAP auto-titration system.

The previously described method for detecting hypopneas may be desirablyadapted to identify milder events because, while the configuration ofeach tidal breath of the hypopnea may be only mildly different, there isa cumulative decline in ventilation or increase in airway resistancewhich often, eventually directly triggers a recovery object orindirectly triggers the occurrence of a recovery object via an arousalresponse. The recovery objects being a precipitous response to a mildbut cumulative decline on airflow is easier to recognize and isexploited to specify timing of the target processing as noted above.

A potential problem with conventional CPAP is that CPAP systemstypically operate with pre-selected criteria for recognition of ahypopnea (such as 50% attenuation of a breath or group of breaths whencompared with a certain number of preceding breaths). These systemsgenerally determine the correct pressures for a given patient bymeasuring parameters derived from the algorithms which monitorparameters through the nasal passage. Unfortunately, the nasal passageresistance is highly variable from patient to patient and may bevariable in a single patient from night to night. These simplisticsingle parameter systems are even less suitable in a hospitalenvironment where many confounding factors (such as sedation or thelike) may severely affect the performance of a conventionalauto-titration system. Since most auto-titration system monitors theireffectiveness through nasal signals their algorithms are limited by thiswide variability of nasal resistance from patient to patient. Studieshave shown that, while apneas can be detected, the detection ofhypopneas by these devices is often poor. This becomes even moreimportant for the detection of mild hypopneas, which can be verydifficult to reliably detect (without an unacceptably high falsepositive rate) through a nasal signal alone. Indeed these milderhypopneas are more difficult characterize and not readily definable as aset of function of a set of predetermined rules for general applicationto all patients. In an exemplary embodiment of the present invention,the system customizes hypopnea recognition to match a given patient'snasal output.

An exemplary embodiment of a process in accordance with the presentinvention suitable for deployment in an auto-titration system isillustrated in FIG. 16. Such a system adjusts its titration algorithm(which can be any of the conventional algorithms) based on theconfigurations of the multi-signal object, which can include oximetrydata, chest wall movement, EEG data sets or the like. In the illustratedsystem, for example, the initial titration algorithm is applied with thedata set of CPAP pressure becoming part of the multi-signal object. Theobject time series at the composite level is monitored for the presenceof persistent clusters (especially clustered recovery objects orclustered EEG arousals). If persistent clusters are identified, then theregion of the cluster occurrences is compared to the identified hypopnearegion derived from the conventional method. If this region is asrecognized as hypopneas, then the pre-selected pressure for a givenincrement in titration is further incremented by 1-2 cm so thatconventional titration occurs at higher-pressure levels. The process maybe repeated until all clusters are eliminated. If EEG arousals worsenwith this increase, then the increment can be withdrawn. If, on theother hand, the algorithm did not recognize this region as a hypopnea,the threshold criteria for a hypopnea is reduced until the clusters areeliminated (some cases require a baseline fixed pressure of 2-3 or morecm). The illustrative embodiment shown in FIG. 16 relates to a CPAPauto-titration system which uses the multi-signal object dataset duringone or more auto-adjusting learning nights to customize a treatmentresponse to a given triggering threshold or the triggering threshold toa given treatment response. The application of a learning night canprevent inappropriate or unnecessary adjustments and can provideimportant information about treatment response while assuring that thebasic algorithm itself is customized to the specific patient upon whomit is applied. This may be useful when using hospital-based monitorswhere the monitor is coupled with the processor of the CPAP unit for thelearning nights while in the hospital. Alternatively, learning nightscan be provided at home by connecting a primary processor for processingmultiple signals with the processor of the CPAP unit for a few nights tooptimize the algorithm for later use. In the hospital, components can beused to attempt to provide optimal titration. Using object-based clusteranalysis of tracing of chest wall impedance and oximetry, the titrationcan be adjusted to assure mitigation of all clusters. In thealternative, if all clusters are not mitigated by the titration then, anurse or other caregiver may be warned that these clusters arerefractory that central apnea should be considered, particularly if theimpedance movements during the apneas are equivocal or low. If, forexample, the patient's oxygen saturation falls (after adjusting for thedelay) in response to an increase in pressure, the pressure can bewithdrawn and the nurse warned that desaturation unresponsive toauto-titration is occurring. If needed, ventilation can be automaticallyinitiated. The self-customizing auto-titration system can include apressure delivery unit capable of auto adjusting either CPAP or BiPAPsuch that such a desaturation in response to CPAP can trigger theautomatic application of BiPAP.

In accordance with embodiments of the present invention, clusters ofhypopneas can generally be reliably recognized utilizing a singleparameter. However, when significant signal noise or reduced gain ispresent, the object-based system can combine matched clusters within atime series of multi-signal objects in the presence of sub-optimalsignals by providing a scoring system for sequential objects. FIGS. 13,14 and 15 are diagrams of schematic object mappings at the compositelevel in accordance with embodiments of the present invention. Theschematics in those figures represent basic cluster matching insituations wherein sub-optimal signals may be present. The multi-signalobjects defining the matched clusters of paired timed datasets ofairflow and oximetry include a matched sequence of negative cycleobjects in the airflow signal and corresponding negative cycle object inthe oximetry signal. Each cycle object is defined by a set of coupledrise and fall objects meeting criteria and occurring within apredetermined interval of each other (as discussed previously). Theoccurrence of a cycle object in either dataset meeting all criteria isgiven a score of one (1). The cycles are counted in sequence for eachmulti-signal cluster object. For the purpose of illustration, inaccordance with embodiments of the present invention, the occurrence ofa score of three (3) in any one signal (meaning that a sequence of three(3) cycles meeting criteria have occurred within a specified interval)provides sufficient evidence to identify a cluster object. When two (2)simultaneous signals are processed, a total score of four (4), derivedfrom adding the number of cycles meeting criteria in each signal, issufficient to indicate the presence of a cluster object. In this manner,the cluster is continued by a sequential unbroken count greater thanthree (3) with one signal, or greater than four (4) with two signals.Once the presence of a cluster object has been established along thetime series, at any point along the cluster object the sequential countalong one signal can be converted to a continuation of the sequentialcount along another signal allowing the cluster object to continueunbroken. The failure of the occurrence of a cycle meeting criteriawithin either signal within a specified interval (for example about90-120 seconds, although other intervals may be used) breaks the clusterobject. A new cluster object is again identified if the count againreaches the thresholds as noted above. It can be seen that this scoringmethod takes into account the fact that artifact often affects onesignal and not another. Therefore, if either signal alone provides asufficient score, the presence of a cluster object is established. Inaddition, the effect of brief episodes of artifact affecting bothsignals is reduced by this scoring method. In this way, artifact, unlessprolonged, may cause the cluster object to be broken but as soon as theartifact has reduced sufficiently in any one or more signals the processof scoring for a new cluster object will restart.

Another CPAP auto-titration system in accordance with embodiments of thepresent invention includes a processor and at least one sensor forsensing a signal transmitted through the nose. Examples of such signalsinclude a pressure signal indicative of airflow, sound, impedance or thelike. An oximeter, which can be detachable or integrated into the CPAPunit, is connected with the processor. The processor detectshypoventilation, using output from both the flow sensor and theoximeter, when the oximeter is attached. In an embodiment in which withthe oximeter is detachable, the processor detects hypoventilation usingthe flow sensor without oximetry when the oximeter is not attached.

In accordance with embodiments of the present invention, themulti-signal object time series can be used for identifyingpathophysiologic divergence Pathophysiologic divergence can be definedat the fundamental, composite, or complex level object. An example ofdivergence at the fundamental level is provided by the relationshipbetween an airflow rise object (inspiration) and a fall object(expiration). Along a time series of matched expiration and inspirationobjects, the occurrence of a marked increase in amplitude of inspirationis commonly associated with an increase in the ratio of the absolutevalue of inspiration slope to the absolute value of the slope ofexhalation. Should this value increase, this provides evidencesuggesting pathophysiologic divergence. Alternatively, in an exemplaryembodiment of the present invention, the evaluation time period can bemuch longer. In one embodiment, the objects defining the data set of thefirst time interval is compared to the objects defining the data set ofthe second corresponding time interval. This comparison is performed ina similar manner to the aforementioned comparison of correspondingcluster objects noted above. The specific parameters, which arecompared, are parameters having known predictable physiologic linkageswherein a change of first physiologic parameter is known to induce arelatively predictable change in a second physiologic parameter. Thesecond parameter is, therefore, a physiologically subordinate of thefirst parameter. As shown in FIG. 11, the first parameter can be ameasure indicative of the timed volume of ventilation and the secondparameter can be the timed arterial oxygen saturation. Here, as shown inFIG. 11, a progressive rise in minute ventilation is expected to producerise in oxygen saturation. The alveolar gas equation, the volume of deadspace ventilation and the oxyhemoglobin disassociation curve predict therise in oxygen saturation by known equations. However, in accordancewith embodiments of the present invention, it is not necessary to knowthe absolute predicted value of oxygen saturation rise for a givenchange in minute ventilation but rather the processor identifies andprovides an output indicating whether or not an expected direction ofchange in the subordinate one parameter occurs in association with agiven direction of change in the primary parameter. For example, withrespect to arterial oxygen saturation and ventilation, embodiments ofthe present invention may determine whether or not an expected directionand/or slope of change of oxygen saturation occur in association with agiven direction and/or slope change in minute ventilation. The timecourse of the rise in ventilation of FIG. 11 is short however, as thetime period lengthens the relationship is strengthened by the greaternumber of corresponding measurements and the greater measurement time.When minute ventilation slopes or trends upward over a sustained period,after the anticipated delay there would be an expected moderate upwardchange in oxygen saturation if the saturation is not already in the highrange of 97-100%. If, on the other hand, if the oxygen saturation isfalling during this period, this would suggest that the patient isexperiencing a divergent pathophysiologic response which may warrantfurther investigation. Automatic recognition of falling or unchangedoxygen saturation in association with a rising minute ventilation canprovide earlier warning of disease than is provided by the simplenon-integrated monitoring and analysis of these two wave forms.

In accordance with embodiments of the present invention, it is notnecessary to be exact with respect to the measurement of minuteventilation. Minute ventilation can be trended by conventional methods,without an absolute determination of the liters per minute for example,by plotting a measure of the amplitude and frequency of a nasal oralthermister or by the application of impedance electrodes on the chest,thereby monitoring the amplitude and frequency of tidal chest movement.Alternatively, conventional impedance or stretch sensitive belts aroundthe chest and abdomen or other measures of chest stall and/or abdominalmovement can be used to monitor tidal ventilation and then this can bemultiplied by the tidal rate of breathing to provide a general index ofthe magnitude of the minute ventilation. In an exemplary embodiment ofthe present invention, the minute ventilation is trended on a time dataset over a five to thirty minute interval along with the oxygensaturation.

In the exemplary embodiment of the present invention shown in FIG. 8,pathophysiologic divergence of timed output may be identified. Asdiscussed previously, the monitor includes a microprocessor 5, the firstsensor 20, a second sensor 25, and an output device 30, which can be adisplay a printer or a combination of both. The processor 5 may beprogrammed to generate a first timed waveform of the first parameter,derived from the first sensor 20, and a second timed waveform of secondparameter, derived from the second sensor 25. Using the multi-signalprocessing system, described previously the processor 5, may be adaptedto compare the objects of the first timed output to the objects of thesecond timed output to identify unexpected divergence of the shape ofthe first timed output to the shape of the second timed output andparticularly to recognize a divergence in directional relationship orpolarity of one timed output of one parameter in relationship to anothertimed output of another related parameter. In an exemplary embodiment,this divergence comprises a fall in the slope of the oxygen saturation(for example, as defined by the recognition of a “decline object”, asdiscussed previously) in relationship to a rise (referred to as a “riseobject”) in the slope of the corresponding minute ventilation. Inanother example, the processor integrates three signals to identifydivergence. The processor identifies the relationship of other signalssuch as heart rate or R-to-R interval or a measure of the pulsemagnitude (as the amplitude, slope of the upstroke, or area under thecurve of the plethesmographic pulse). In particular, a rise object inminute ventilation may be identified in association with a declineobject in oxygen saturation and a decline object in heart rate or pulseamplitude. These outputs can be plotted on a display 30 for furtherinterpretation by a physician with the point of pathophysiologicdivergence of one parameter in relationship to another parameteridentified by a textural or other marker.

The identification of pathophysiologic divergence can result insignificant false alarms if applied to the short time intervals used forrise and decline objects which are used for detection of cluster objects(and also the short averaging intervals for this purpose). Inparticular, if the identification of divergence is applied for shortintervals, such as one (1) to two (2) minutes, a significant number offalse episodes of divergence may be identified. In accordance withembodiments of the present invention, clear evidence of a trend in onemeasured parameter in relationship to a trend of another measuredparameter may be provided so that it is likely that divergence hasindeed occurred. This can be enhanced by the evaluation of the prolongedgeneral shape or polarity of the signal so that it is consideredpreferable to identify divergence over segments of five to thirtyminutes. The averaging of many composite objects to identify a riseobject at the complex object level helps mitigate such false alarms. Forthis reason, the expected time course of a divergence type must bematched with the resolution (or averaging times) of the objectscompared.

According to an exemplary embodiment of the present invention, toenhance the reliability of the analysis of the timed data set, theaveraging interval for this purpose, can be adjusted to avoid excessivetriggering of the intermittent monitoring device. In one exemplaryembodiment, the averaging interval is increased to between thirty andninety seconds or only the analysis of complex objects can be specified.Alternative methods may be used to identify a rise and fall objects suchas the application of line of best-fit formulas, as previouslydiscussed. Elimination of outlier data points to define larger compositeobjects can also be applied as also previously discussed or by othermethods. In this way, the identification of a trend change, whichevolves over a period of five to fifteen minutes, can be readilyidentified. The identification of divergence can produce a textualoutput, which can be maintained for a finite period until the secondaryparameter corrects or a threshold period of time has elapsed. Forexample, if a rise in minute ventilation is identified over apredetermined interval period (such as about ten minutes) to define arise object and a fall in oxygen saturation is identified over acorresponding period to define a fall object, the processor identifiesthe presence of divergence and can produce a textual output which can beprovided on the bedside display or central processing display. Thistextual output can be maintained for a finite period, for example, oneto two hours, unless the oxygen saturation returns to near its previousvalue, at which time the textual output may be withdrawn from thedisplay.

In this manner, the presence of pathophysiologic divergence is readilyidentified. However, since divergence is defined by divergent rise andfall objects of corresponding physiologically linked parameters, itsduration is necessarily limited since these slopes cannot continue todiverge indefinitely. It is important to carry forward theidentification of prior divergence in the patient's display for at leasta limited period of time so that the nurse can be aware that this eventhas occurred. For example, a “fall object” identified in the secondary,signal such as a fall in oxygen saturation from 95% to 90% over a periodof ten minutes occurring in association with a rise object in theprimary signal, such as, for example, a doubling of the amplitude of theairflow or chest wall impedance deflection over a period of ten minutescan produce an identification of pathophysiologic divergence that can belinked to the outputted saturation so that the display shows asaturation of 90% providing an associated textual statement“divergence-TIME”. This identification of divergence can, over a periodof time, be withdrawn from the display or it can be immediatelywithdrawn if the oxygen saturation corrects back close to 95%.

As discussed previously and as also illustrated in FIG. 8, in anotherexemplary embodiment of the present invention, a change in theconfiguration of the multi-signal time series can be used to trigger theaddition of one or more additional signals to the multi-signal timeseries, such as a non-invasive blood pressure. In this manner, a systemcan identify whether pathophysiologic divergence is occurring withrespect to the new, less frequently sampled signal. For example, thetrending rise in heart rate should not be generally associated with afall in blood pressure. If, for example over a period of 5 to 20minutes, a significant rise in heart rate (as for example a 25% rise andat least 15 beats per minute) is identified by the processor, themonitor can automatically trigger the controller of a non-invasive bloodpressure monitor to cause the measurement of blood pressure to beimmediately taken. The output of the non-invasive blood pressure monitoris then compared by the processor to the previous value which wasrecorded from the blood pressure monitor and, if a significant fall inblood pressure (such as a fall in systolic of 15% and more) isidentified in association with the identified rise in heart rate whichtriggered the test, a textual warning can be provided indicating thatthe patient is experiencing pathophysiologic divergence with respect toheart rate and blood pressure so that early action can be taken beforeeither of these values reach life-threatening levels. According toanother embodiment of the present invention, a timed dataset of thepulse rate is analyzed, if a significant change (for example, a 30-50%increase in the rate or a 30-50% decrease in the interval or a 50-75%increase in the variability of the rate), then the blood pressuremonitor can be triggered to determine if a significant change in bloodpressure has occurred in relation to the change in pulse rate or theR-to-R interval. This can be threshold adjusted. For instance, asignificant rise in heart rate of 50%, if lasting for a period of twoand a half minutes, can be used to trigger the intermittent monitor. Onthe other hand, a more modest rise in heart rate of, for example, 25%may require a period of five or more minutes before the intermittentmonitor is triggered.

In another embodiment, also represented in FIG. 8, identification by thebedside processor 5 of a sustained fall in oxygen saturation can be usedto trigger an ex-vivo monitor 40 to automatically measure the arterialblood gas parameters. Alternatively, a significant rise in respiratoryrate (for example, a 100% increase in respiratory rate for five minutes)can suffice as a trigger to automatically evaluate either the bloodpressure or an ex-vivo monitor of arterial blood gasses.

There are vulnerabilities of certain qualitative indexes of minuteventilation in relationship to divergence, the effect of which may bereduced by embodiments of the present invention serves to enhance theclinical applicability of the output. For example, a rise in the signalfrom chest wall impedance can be associated with a change in bodyposition. Furthermore, a change in body position could result in a fallof oxygen saturation due to alteration in the level of ventilation,particularly in obese patients. Such alterations can be associated withan alteration in the ventilation perfusion matching in patients withregional lung disease. Therefore, a change in body position couldproduce a false physiologic divergence of the signals when themulti-signal time series includes chest wall impedance and oximetry. Forthis reason, in accordance with embodiments of the present invention,additional time series components may be employed, such as informationprovided by a position sensor. Alternatively, if position information isnot available, a more significant fall in one parameter may be used inassociation with a more significant divergent rise in another. By way ofexample, a significant fall in oxygen saturation of, for example, 4-5%in association with a doubling of the product of the amplitude andfrequency of the impedance monitor would provide evidence that thispatient is experiencing significant pathophysiologic divergence andwould be an indication for a textual output indicating thatpathophysiologic divergence has occurred. The thresholds for definingdivergence, in accordance with embodiments of the present invention, maybe selectable by the physician or nurse. When the time series output ofa position monitor is incorporated into the system with a significantposition-related change in one or more parameters, the position monitorprovides useful additional information.

In accordance with embodiments of the present invention, the magnitudeof pathophysiologic divergence can be provided on the central display 38or bedside display 30. In some cases, as discussed previously, a milddegree of pathophysiologic divergence may not represent a significantchange and the nurse may instead want to see an index of the degree ofpathophysiologic divergence. A bar graph or other variable indicator,which can be on the order of the monitoring cubes of illustrated inFIGS. 6 a-6 e, can provide this. In one embodiment the monitoring cubecan be selectively time-lapsed to observe the previous relationalchanges between parameters. Alternatively, the animated object can berotated and scaled to visually enhance the represented timedrelationships and points of divergence.

In one embodiment of the present invention, the multi-signal time seriesoutput is placed into a format useful for reviewing events preceding anarrest or for physician or nurse education. In this format, the outputcontrols an animation of multiple objects which, instead of being partsof a hexagon or cube, are shaped into an animated schematic of the asthe physiologic system being monitored. The animation moves over timeand in response to the signals in one preferred embodiment. The type ofsignals (or the reliability of such signals) determines which componentsof the schematic are “turned on” and visible. One example includes amulti-signal object defined by outputs of airflow, thoracic impedance,oximetry, and blood pressure, rendering a connected set of animationobjects for the lungs, upper airway, lower airway, heart, and bloodvessels which can be animated as set forth below in Table 2: TABLE 2Each inspiration causing an animated enlargement of the lungs trackingthe inspiration slope Each expiration causing an animated reduction insize of the lungs tracking the expiration slope Each animated systolicbeat of the heart tracks the QRS or upstroke of the oximetry output Thecolor of the blood in the arteries and left heart tracks the oxygensaturation The diameter of the lower airway (a narrowing diameter can behighlighted in red) tracks the determination of obstruction by the sloperatio in situations of hyperventilation (as discussed previously) Thepatency of the upper airway (a narrowing or closure can be highlightedin red) tracks the determination of upper airway obstruction (asdiscussed previously) The magnitude of an animated pressure gauge tracksthe blood pressure

This provides “physiologic animation” which can be monitored inreal-time but will generally be derived and reviewed from the storedmulti-signal objects at variable time scales. This is another example ofan embodiment of the present invention providing a quickly, easilyunderstood and dynamic animated output of a highly complex, interactivetime series derived form a patient. The animation can be reviewed at anincreased time lapsed rate to speed through evolution of a givenpatients outputs or can be slowed or stopped to see the actual globalphysiologic state at the point of arrhythmia onset.

In another example, a patient with a drop in oxygen saturation of 4% anda doubling of the product of the frequency and amplitude of the chestwall impedance tidal variation may have a single bar presented on themonitor, whereas a patient with a 6% drop wherein the product of theimpedance amplitude and frequency has tripled may have a double bar, andso on. This allows reduction in the occurrence of false alarms byproviding a bar indicator of the degree of divergence that has occurred.A similar indicator can be provided for clustering, indicative of theseverity of airway or ventilation instability. It should be noted thatvery mild clustering may simply represent the effect of moderatesedation, and not, therefore, represent a cause for great concern(although it is important to recognize that it is present). Such aclustering could be identified with a single bar, whereas more severeclustering would generate a larger warning and, it very severe, anauditory alarm. When the clustering becomes more severe and demonstratesgreater levels of desaturation and/or shorter recovery intervals, thebar can be doubled.

In another embodiment, which could be useful for neonates, the timeseries of multi-signal objects is derived entirely from a pulseoximeter. Each object level for each signal and further a multi-signalobject time series of the oxygen saturation and pulse (as for examplecan be calculated below) is derived. This particular multi-signal timeseries has specific utility for severity indexing of apnea ofprematurity. The reason for this is that the diving reflex in neonatesand infants is very strong and causes significant, cumulativebradycardia having a progressive down slope upon the cessation. Inaddition, the apnea is associated with significant hypoxemia, which alsocauses a rapid down slope due to low oxygen storage of these tinyinfants. Even a few seconds of prolongation of apnea causes profoundbradycardia because the fall in heart rate like that of the oxygensaturation does, not have a reliable limit or nadir but rather fallsthroughout the apnea. These episodes of bradycardia cluster in a manneralmost identical to that of the oxygen saturation, the pulse in theneonate being a direct subordinate to respiration.

In neonates, oxygen delivery to the brain is dependent both upon thearterial oxygen saturation and the cardiac output. Since bradycardia isassociated with a significant fall in cardiac output, oxygen delivery tothe neonatal brain is reduced both by the bradycardia and the fall inoxygen saturation. It is critical to have time series measurements,which relate to cumulative oxygen delivery (or the deficit thereof) bothas a function of pulse and oxygen saturation. Although many indices canbe derived within the scope of the present invention, the presentlypreferred index is given as the “Saturation Pulse”. Although manycalculations of this index are possible, in one exemplary embodiment ofthe present invention, the index is calculated as:SP=R(SO2-25)

Where:

-   -   SP is the saturation pulse in “% beats/sec”    -   R is the instantaneous heart rate in beats per second, and    -   SO2 is the oxygen saturation of arterial blood in %.

The saturation-pulse is directly related to the brain oxygen delivery.The SpO₂-25 is chosen because 25% approaches the limit of extractableoxygen in the neonatal brain. The index is preferably counted for eachconsecutive acquisition of saturation and pulse to produce a continuoustime series (which is an integral part of a multi-signal time series ofoxygen saturation and pulse). This index can be calculated for the timeinterval of each apnea and each cluster to derive an apnea or clusterindex of saturation-pulse during apnea and recovery in a manneranalogous to that described in U.S. Pat. No. 6,223,064, which is herebyincorporated by reference herein. This provides an enhanced tool forseverity indexing of apnea of prematurity in infants. Both the durationand the absolute value of any decrement in saturation-pulse arerelevant. If desired, the average maximum instantaneous and cumulativedeficit of the pulse saturation index can be calculated for each cluster(as by comparing to predicted normal or automatically calculated, nonapnea related baseline values for a given patient).

In this way, in accordance with embodiments of the present invention, ageneral estimate of oxygen delivery over time to the infant brain isprovided using a non-invasive pulse oximeter. This estimate is derivedthrough the calculation of both oxygen saturation and pulse over anextended time series deriving a cumulative deficit specifically withinclusters of apneas to determine index of the total extent of globaldecrease in oxygen delivery to the brain during apnea clusters. Thedeficit can be calculated in relation to either the baseline saturationand pulse rate or predicted normals.

The processor can provide an output indicative of the pulse saturationindex, which can include an alarm, or the processor can trigger anautomatic stimulation mechanism to the neonate, which will arouse theneonate thereby aborting the apnea cluster. Stimulation can include atactile stimulator such as a vibratory stimulator or other device, whichpreferably provides painless stimulation to the infant, thereby causingthe infant to arouse and abort the apnea cluster.

In another embodiment of the present invention, the recognition of aparticular configuration and/or order of objects can trigger thecollection of additional data points of another parameter so that thesenew data points can be added to and compared with the original timeseries to recognize or confirm an evolving pathophysiologic process. Oneapplication of this type of system is shown in FIG. 8 and illustratedfurther in FIG. 17. The time series of pulse, oxygen saturation, and/orcardiac rhythm can be used to trigger an automatic evaluation of bloodpressure by a non-invasive blood pressure device. The bedside processor,upon recognition of tachycardia by evaluation of the pulse or EKGtracing, automatically causes the controller of the secondary monitoringdevice 40 to initiate testing. The nurse is then immediately notifiednot only of the occurrence, but also is automatically provided with anindication of the hemodynamic significance of this arrhythmia. In thissituation, for example, the occurrence of an arrhythmia lasting for atleast twenty seconds can trigger the automatic comparison of the mostrecent blood pressure antecedent the arrhythmia and the subsequent bloodpressure, which occurred after the initiation of the arrhythmia. Theprocessor identifies the time of the initial blood pressure, whichoccurred prior to the point of onset of the arrhythmia, and the time ofevaluation of the blood pressure after the onset of the arrhythmia.These parameters may be provided in a textural output so that the nursecan immediately recognize the hemodynamic significance of thearrhythmia. Upon the development of a pulseless arrhythmia, a printedoutput is triggered which provides a summary of the parameter valuesover a range (such as the 5-20 minutes) prior to the event as well as atthe moment of the event. These are provided in a graphical format to beimmediately available to the nurse and physician at the bedside duringthe resuscitation efforts so that the physician is immediately aware ifhyperventilation, or oxygen desaturation preceded the arrhythmia (whichcan mean that alternative therapy is indicated.

In accordance with another aspect of the present invention, if thepatient does not have a non-invasive blood pressure cuff monitorattached, but rather has only a pulse oximeter or an impedance basednon-invasive cardiac output monitor and an electrocardiogram attached,then the multi-level time series plethsmographic pulse objects can beused to help determine the hemodynamic significance of a given change inheart rate or the development of an arrhythmia. In this manner, theidentification of significant change in the area under the curveassociated with a significant rise in heart rate or the development ofan arrhythmia can comprises a multi-signal object indicative ofpotential hemodynamic instability.

If the multi-signal object includes a new time series of wide QRScomplexes of this occurrence is compared to the area under theplethesmographic pulse to determine the presence of “pulseless” or “nearpulseless” tachycardia. It is critical to identify early pulselesstachycardia (particularly ventricular tachycardia) since cardioversionof pulseless tachycardia may be more effective than the cardioversion ofventricular fibrillation. On the other hand, ventricular tachycardiaassociated with an effective pulse, in some situations, may not requirecardioversion and may be treated medically. Timing in both situations isimportant since myocardial lactic acidosis and irreversibleintracellular changes rapidly develop and this reduces effectivecardioversion. It is, therefore, very important to immediately recognizewhether or not the significant precipitous increase in heart rate isassociated with an effective pulse.

The plethesmographic tracing of the oximeter can provide indication ofthe presence or absence of an effective pulse. However, displacement ofthe oximeter from the proper position on the digit can also result inloss of the plethesmographic tracing. For this reason, in accordancewith embodiments of the present invention, the exact time in which thewide QRS complex time series developed is identified and related to thetime of the loss of the plethesmographic pulse. If the plethesmographicpulse is lost immediately upon occurrence of a sudden increase of heartrate (provided that the signal does not indicate displacement), this isnearly definitive evidence that this is a pulseless rhythm and requirescardioversion. The oxygen saturation and thoracic impedance portion ofthe multi-signal object is also considered relevant for theidentification of the cause of arrhythmia. At that moment, an automaticexternal cardioversion device can be triggered to convert the pulselessrhythm. In an alternative embodiment, as also shown in FIG. 17, a bloodpressure monitor, which can be a non-invasive blood pressure monitorintegrated with the automatic defibrillator, can be provided. Upon therecognition of a precipitous increase in heart rate, this event cantrigger automatic non-invasive blood pressure evaluation. If thenon-invasive blood pressure evaluation identifies the absence ofsignificant blood pressure and pulse confirmed by the absence of aplethesmographic pulse, then the processor can signal the controller ofthe automatic cardio version unit to apply and electrical shock to thepatient based on these findings. It can be seen that multiple levels ofdiscretionary analysis can be applied. A first level of analysis couldbe the identification of a precipitous development of a wide complextachyarrhythmia in association with simultaneous loss ofplethesmographic pulse which can trigger an automatic synchronizedexternal cardio version before the patient develops ventricularfibrillation. A second level of analysis could include confirmation byanother secondary measurement such as loss of blood pressure, the lackof the anticipated cycle of chest impedance variation associated withnormal cardiac output as with a continuous cardiac output monitor, orother quality or confidence indicator.

It can be seen that even without the EKG time series component object ananalysis of the multi-signal object can be applied to compare the areaunder the curve of the plethesmographic pulse tracing generated by apulse oximeter to a plot of peak-to-peak interval of the pulse tracings.The sudden decrease in the peak-to-peak interval or increase in pulserate in association with a sudden decrease in the plethesmographic areais strong evidence that the patient has experienced a hemodynamicallysignificant cardiac arrhythmia. In the alternative, a moderate andslowly trending upward increase in heart rate in association with amoderate and slowly trending downward plot of the area of theplethesmographic pulse would be consistent with intervascular volumedepletion, or ineffective cardiac output resulting from significantsympathetic stimulation which is reducing the perfusion of theextremities as with as congestive heart failure. During such a slowevolution, it would also be anticipated that the frequency of tidalrespirations would increase.

Those skilled in the art will recognize that the information providedfrom the data and analysis generated from the above-described system canform the basis for other hardware and/or software systems and has widepotential utility. Devices and/or software can provide input to or actas a consumer of the physiologic signal processing system of the presentinvention's data and analysis.

Table 3, set forth below, provides a non-exhaustive list of examples ofexemplary ways that the present physiologic signal processing system caninteract with other hardware or software systems: TABLE 3 1. Softwaresystems can produce data in the form of a waveform that can be consumedby the physiologic signal processing system 2. Embedded systems inhardware devices can produce a real-time stream of data to be consumedby the physiologic signal processing system 3. Software systems canaccess the physiologic signal processing system representations ofpopulations of patients for statistical analysis 4. Software systems canaccess the physiologic signal processing system for conditions requiringhardware responses (e.g. increased pressure in a CPAP device), signalthe necessary adjustment and then analyze the resulting physiologicalresponse through continuous reading of the physiologic signal processingsystem data and analysis

It is anticipated that the physiologic signal processing system will beused in these and many other ways. To facilitate this anticipatedextension through related hardware and software systems the presentsystem will provide an application program interface (API). This API canbe provided through extendable source code objects, programmablecomponents and/or a set of services. Access can be tightly coupledthrough software language mechanisms (e.g. a set of C++ modules or Javaclasses) or proprietary operating system protocols (e.g. Microsoft'sDCOM, OMG's CORBA or the Sun Java Platform) or can be loosely coupledthrough industry standard non-proprietary protocols that providereal-time discovery and invocation (e.g. SOAP [Simple Object AccessProtocol] or WSDL [Web Service Definition Language]).

In accordance with an exemplary embodiment of the present invention, thephysiologic signal processing system with the API as defined becomes aset of programmable objects providing a feature-rich development andoperating environment for future software creation and hardwareintegration.

Although embodiments in accordance with the present invention have beendescribed, which relate to the processing of physiologic signals, it isalso critical to recognize the present streaming parallel objects baseddata organization and processing method can be used to order and analyzea wide range of dynamic patterns of interactions across a wide range ofcorresponding signals and data sets in many environments. The inventionis especially applicable to the monitoring of the variations or changesto a physical system, biologic system, or machine subjected to aspecific process or group of processes over a specific time interval.

Embodiments of the present invention may provide a general platform forthe organization and analysis of a very wide range of datasets duringhospitalization or a surgical procedure. For example, in addition to thetime series of the monitored signals parameters, which may be sampled ata wide range (for example between about 500 hertz and 0.01 hertz),previously noted, the cylindrical data matrix can include a plurality oftime series of laboratory data, which may be sampled on a daily basis oronly once during the hospitalization. These data points or time seriesare stored as objects and can be included in the analysis. These objectscan include, for example, the results of an echocardiogram wherein atimed value ejection fraction of the left ventricle is provided as anobject in the matrix for comparison with other relationships. Inapplication, the presence of a low ejection fraction object along thematrix with a particular dynamic cyclic variation relationship betweenairflow and oxygen saturation time series can, for example, providestrong evidence of periodic breathing secondary to congestive heartfailure and this identified relationship can be provided for thehealthcare worker in a textual output. In another example, medicationdata is included in data matrix. For example, in a patient receivingdigoxin and furosemide (a diuretic), the daily serum potassium timeseries is compared to a time series indicative of the number andseverity of ventricular arrhythmias such as premature ventricularcontractions. A fall in the slope of the potassium time series inassociation with a rise in slope of such an arrhythmia indication timeseries could for example produce an output such as “increasedPVCs—possibly secondary to falling potassium, consider checking digoxinlevel.” In another example, a first time series of the total carbondioxide level and a second time series of the anion gap can be includedin the general streaming object matrix and compared to the time seriesof airflow. If a rise in the slope or absolute values of the airflow isidentified with a fall in the slope or absolute value along the totalcarbon-dioxide time series and a rise the slope or absolute values alonethe anion gap time series, the processor can provide an automaticidentification that the airflow is rising and that the cause of a risein airflow may be secondary to the development of a potentially lifethreatening acidosis, providing an output such as“hyperventilation—possibly due to evolving anion gap acidosis”. Inanother example, the daily weight or net fluid balance is included withthe total carbon dioxide and anion gap in the cylindrical data matrix.The identification of a fall in slope of airflow or absolute value alongwith a fall in slope of the oxygen saturation, and a fall in slope ofthe fluid balance and weight can generate an output such as “possiblehypoventilation-consider contraction alkalosis.”

Alternatively with a matrix made up of the same parameters, a rise inthe slope or absolute values of the airflow time series and a rise inthe pulse time series may be recognized in comparison with a fall in thetime series of the total carbon dioxide, a flat slope of the time seriesof the anion gap, and a rise in the slope or absolute values of thefluid balance time series, confirmed by a trending rise in slope of theweight time series, and a notification can be provided as“hyperventilation—potentially secondary to expansion acidosis orcongestive heart failure.” In one exemplary embodiment of the presentinvention, the cylindrical data matrix becomes the platform upon whichsubstantially all relevant data derived during a hospitalization isstored and processed for discretionary and automatic comparison. Initialinput values, which can be historical input, can also be included to setthe initial state of the data matrix. For example, if the patient isknown to have a history congestive heart failure, and that condition isaccounted for as an initial data point at the start of the matrix, aparticular conformation in the initial matrix may be considered in theanalysis. The data matrix provides a powerful tool to compare the onsetof dynamic changes in parameters with any external force acting on theorganism whether this force is pharmacological, a procedure, related tofluid balance, or even simple transportation to other departments fortesting. In one exemplary embodiment, as shown in FIG. 1 b, a timeseries of action applied to the patient is included in a time seriesthat may be referred to as an “exogenous action time series.” This timeseries includes a set of streaming objects indicating the actions beingapplied to the patient throughout the hospitalization. In this example,within the exogenous action time series, a time series componentindicative of dynamic occurrence of a particular invasive procedure,such as the performance of bronchoscopy, is included. This“bronchoscopic procedure object” may, for example, comprise a timeseries component along the exogenous action time series of 15 minuteswithin the total matrix derived from the hospitalization. The dynamicrelationships of the parameters along the matrix are compared with theonset of the procedure (which comprises an object onset), dynamicpatterns of interaction evolving subsequent to the onset of theprocedure can be identified and the temporal relationship to theprocedure object identified and outputted in a similar manner as hasbeen described above for other objects. The dynamic patterns ofinteraction can be interpreted with consideration of the type ofprocedure applied. For example, after a 15 minute time series associatedwith a bronchoscopic procedure, the occurrence of a progressive increasein slope of the airflow time series associated with a significantdecrease in the slope of the inspiration to expiration slope ratio timeseries suggests the development of bronchospasm secondary to thebronchoscopy and can initiate an output such as “hyperventilationpost-bronchoscopy with decreased I:E—consider bronchospasm.”

A larger surgical procedure comprises a longer cylindrical data matrixand this can comprise a perioperative matrix, which can include theportion of time beginning with the administration of the firstpreoperative medication so that dynamic patterns of interaction arecompared with consideration of the perioperative period as a global timeseries object within the matrix. The preoperative period, the operativeperiod, and the post operative period may be identified as differenttime-series segments of the matrix within the total hospital matrix.Using this object-based relational approach, a “dynamic pattern” ofinteraction occurring within this procedure-related data stream orsubsequent to it can be easily recognized. The dynamic pattern may thenbe temporally correlated with the procedure so that the dynamicrelationships between a procedure and plurality of monitored time seriesoutputs and/or laboratory data are stored, analyzed, and outputted. Inanother example, the continuous or intermittent infusion of apharmaceutical such as a sedative, narcotic, or inotropic drug comprisesa time series which has as one of its timed characteristics the doseadministered. This new time series is added to the cylindrical matrixand the dynamic relationships between monitored signals and laboratorydata is compared. For example after the initiation of Dobutamine (aninotropic drug) the occurrence of a rising slope of pulse rate or arising slope of premature ventricular contraction frequency, or theoccurrence of an object of non-sustained ventricular tachycardia, can berecognized in relation to onset the time series of medication infusionor a particular rise in the slope or absolute value of the of the doseof this medication. In another example, the occurrence of a dynamicclustering of apneas such as those presented in FIGS. 10, 11, and 5 c inrelation to a rise in slope, or a particular absolute value, of the timeseries of the sedative infusion can he identified and the pump can beautomatically locked out to prevent further infusion. An output such as“Caution—pattern suggestive of mild upper airway instability at dose of1 mg Versed” may be displayed and/or printed. If, in this example, thenurse increases the dose to 2 mg and the pattern shows an increase inseverity, an output such as “Pattern suggestive of moderated upperairway instability at dose of 2 mg/hr. of Versed-dose locked out” may bedisplayed and/or printed. To maintain Versed dose at the 2 mg level inthis patient, the nurse or physician would have to override the lockout.Upon an override, the processor then tracks the severity of the clustersand, if the clusters reach a additional severity threshold then anoutput such as “Severe upper airway instability—Versed locked out” maybe displayed and/or printed.

The anticipated range of time series for incorporation into thecylindrical relational matrix of streaming objects include multiplepharmaceutical time series, exogenous action time series, monitoredsignal time series (which can include virtually any monitored parameteror its derivative), fluid balance, weight, and temperature time series.Time series or single timed data points of laboratory values (includingchemistry, hematology, drug level monitoring), and procedure basedoutputs (such as echocardiogram and pulmonary function test outputs) mayalso be included. Interpreted radiology results may also be incorporatedas data points and once the digital signal for such testing can bereasonably summarized to produce a time series, which reliably reflectsa trend (such as the degree of pulmonary congestion). Such outputs canalso be included in the data matrix as time series for comparison withfor example the net fluid balance and weight time series. An additionaltime series can be the provided by nursing input, for example, a timeseries of the pain index, or Ramsey Scale based level of sedation. Thistime series can be correlated with other monitored indices of sedationor anesthesia as is known in the art.

The cylindrical matrix of processed, analyzed, and objectified dataprovides a useful tool for the purpose of doing business to determine,much more exactly, the dynamic factors, occurrences, and patterns ofrelationships, which increase expense in any timed process. In theexample of the hospital system discussed above, the expense data isstructured as a time series of objects with the data point valuerepresented by the total expense at each point. Expense values can belinked and/or derived from certain procedures or laboratory tests, forexample the time series of the hemoglobin can be associated with acorresponding time series of the calculated expense for that test. In anexemplary embodiment, the plurality of time series of expenses for eachmonitored laboratory tests are combined to produce a global expense timeseries. Individual time series for the expense of each class ofexogenous actions (such as pharmaceutical, and procedural time series)may also be provided and can then be combined to form one global expensetime series. This may be incorporated into the cylindrical data matrixto provide discretionary comparison with dynamic expense variables anddynamic patterns of relationships of other variables. This allows thehospital to determine the immediate expense related to the occurrence ofan episode of ventricular fibrillation. This expense can be correlatedwith, for example, the timeliness of treatment, the application ofdifferent technologies, or the presence of a specific dynamic pattern ofinteraction of the signals. In other words, the immediate cost, andresources expended over, for example, the 24 hours following the episodeof ventricular fibrillation, can be compared with the true behavior andduration of the pathophysiologic components relating the ventricularfibrillation episode.

In a further example consider a patient monitored with an embodiment ofthe present invention deriving a cylindrical data matrix comprised ofstreaming and overlapping objects of airflow, chest wall impedance, EKG,oximetry, and global expense. The occurrence of the procedure forinsertion of the central line represents an object (which need not havea variable value) along a segment of the cylinder. If the patentdevelops a pneumothorax, the processor can early identify and warn ofthe development of pathophysiologic divergence with respect to theairflow (and/or chest wall impedance) and the oxygen saturation (and/orpulse). In addition to earlier recognition, the expense related to thiscomplication, the timeliness of intervention, the magnitude ofpathophysiologic perturbation due to the complication, and the resourcesexpended to correct the complication can all be readily determined usingthe processor method and data structure of the present invention.

In a further example, consider a patient monitored with an embodiment ofthe present invention deriving a cylindrical data matrix comprised ofstreaming and overlapping objects of airflow, chest wall impedance, EKG,oximetry, and global expense. The occurrence of the procedure forinsertion of the central line represents an object (which need not havea variable value) along a segment of the cylinder. If the patentdevelops a pneumothorax, the processor can early identify and warn ofthe development of pathophysiologic divergence with respect to theairflow (and/or chest wall impedance) and the oxygen saturation (and/orpulse). In addition to earlier recognition, the expense related to thiscomplication, the timeliness of intervention, the magnitude ofpathophysiologic perturbation due to the complication, and the resourcesexpended to correct the complication can all be readily determined usingthe processor method and data structure in accordance with embodimentsof the present invention.

Many other additional new component time series and “cylinders ofascending parallel time series” may be added to the matrix. During theimplementation of the present invention it is anticipated that manysubtle relationships between the many components will become evident tothose skilled in the art and these are included within the scope of thisinvention.

Many indices of instability are definable within the scope of thisteaching. One example of a useful mathematical relationship calculatedby a system in accordance with an exemplary embodiment of the presentinvention may be derived as set forth below.

The percentage of time during the selected measurement interval whereinthe airway is functionally unstable (and cycling) can be defined by theratio:Tu/Ttwhere Tu is a time unstable, and Tt is a time total.However all unstable time is not equal. To quantify the severity ofinstability within Tu the following formula is applied:Tr/Tcwhere Tr is a time of recovery and Tct is a time of closure.

For example, when SPO2 is the parameter under test, this would be(Duration of SPO2 Fall)/(Duration from beginning of next SPO2 rise tobeginning of next fall). From this can be derived a “ratio ofinstability ratios” as:VII=[Tu/Tt]/[Tr/Tc]where VII is the Ventilation Instability Index (or HypoventilationInstability Index or Instability Index).

This Instability Index is parameter nonspecific and is applicable to awide range of discretionary time intervals and to the time intervalselected for the test, which may be selectable, for example, using amenu. The index can then be adjusted for the parameter under test (e.g.SPO2 or Pulse) rendering, for example, a desaturation instability index,a heart rate instability index or a pulse amplitude instability index toname a few. Indeed, the instability index can be calculated and adjustedfor non-medical signals such as signals of movement, signals ofelectromagnetic energy, biologic time series, mechanical time series,and financial signals, such as a time series of a financial index toname a few.

In one example of this adjustment we multiply the VVI by the meanperturbation amplitude of the target event under test (for example theDesaturation instability index is calculated as:DVII=(D)Tu/Tt]/[Tr/Tc]Where D=mean amplitude of the fall events within the clusters (note thatdPulse amplitude, or dheart rate would be used for those indices ifdesired).

In one example, DAVII can be calculated as: (10) (D) [Tu/Tt]/[Tr/Tc].For this example, it is desirable that the recovery (interapneainterval) is not the mean interapnea interval for the time period undertest but rather the mean interapnea interval within the unstable(cycling) time and excludes recovery time between clusters.

In one exemplary embodiment, the analysis system determines an “SPO2Wake-Sleep Gradient,” an “SPO2 Wake-REM gradient” and/or an “SPO2 NonREM-REM gradient.” To achieve this, the system can, for example,determine a baseline value of SPO2 (as, for example, over a 5 second to15 minute period of resting wakefulness, although other time periods maybe used) and then determine the SPO2 value during a period of sleep, (ora period a particular sleep stage such as REM) wherein the SPO2 value isnot exhibiting a cluster pattern (for example this may be the average ofthe SPO2 over a 5 second to 15 minute period of REM). This determinationcan for example be the point or range of points immediately precedingthe onset of the first cluster after sleep onset. According to oneaspect of the present invention, the difference between the awake SPO2and the sleep SPO2 is outputted as by print or other output. This valueis different than the average value of SPO2 during wakefulness or asleep stage, which includes cluster related values. The SPO2 Wake-SleepGradient can be a useful indication of the stability of ventilatorycontrol during sleep. For example, patients with SPO2 Wake-SleepGradients of 4%-8% or more are often exhibiting a significant decline inventilation control in response to assumption of a given sleep stateespecially if this decline is progressive on sleep onset, and/or endswith the onset of a cluster. This sleep onset related fall inventilation and/or SPO2 which ends in the onset of a cluster pattern maybe called a “Pattern of Transitional Instability” and provides evidenceconcerning the phenotype of the patient's sleep disordered breathing, asdefined by suboptimal central ventilation control during sleep. Suchpatients may be at risk for exhibiting severe worsening later in sleepwith incomplete arousals especially during REM sleep. A Wake-SleepGradient and the detection of the Pattern of Transitional Instabilitycan be similarly achieved by analysis of airflow parameters (as, forexample, by CO2 or minute ventilation to name a few) and fortranscutaneous parameters such as CO2 and/or PO2.

In another embodiment, an implanted pacemaker may be programmed torecord the ventilation patterns with a sampling rate sufficient todetect the instability clusters. The recording can be continuous orintermittent and can be triggered by an internal clock or by an actionor inaction of the patient (for example, as by sensors recognizing theassumption of a specific type or range of body positions, such ashorizontal body position, or a sensor detecting a lack of movement (asby incorporated atigraphy)). In one embodiment, the pacemaker stores theventilation data sets for automatic or manual retrieval by an externalpacemaker interrogator. The datasets are then analyzed for detection andseverity indexing of Cheynne Stokes respirations or sleep apnea, whichcan be useful in monitoring the severity of heart failure. This providesa method for monitoring heart failure and for detecting sleep disorderedbreathing in patients with indwelling pacemakers. The interrogator caninclude memory for storage of the ventilation datasets and software foranalyzing those data sets for sleep disordered breathing as by detectingclusters. In one method, a pulse oximeter probe is applied to a bodypart and the overnight time series of oxygen derived from the oximeterand the timed datasets of EKG, ST segment position, and ventilationderived from the pacemaker are compared with a contemporaneously timeseries from the oximeter.

In an exemplary embodiment of the present invention, a signal patternviewer is provided. This viewer is preferably accessible directly fromthe patient's medical record in the hospital information system or from,for example, the display of a mechanical ventilator. When a healthcareworker requests access, the viewer component loads and accesses thephysiologic time series data from a local database or through servicesto a centralized data repository into which the time series from thepatient monitors are continuously or intermittently updated and stored.The view component processes and analyzes the physiologic time seriesdata and the image of the processed signals is then displayed. In oneexample, this image can be a pre-selected grouping of processed andanalyzed parallel time series of different parameters or can be a groupof parameters indicated through a set of user gestures. Upon loading,the processor can then output a series of two-dimensional cross-sectionsdefining three-dimensional representation of the parameters. This can beaccomplished, for example by displaying the parameters in differentquadrants of a two-dimensional representation, which can be renderedwith the points connected to enhance the image. The incremental distancevalues from the center of the all the intersecting quadrants can bedefined for each parameter by placing the normal ranges equidistant formthe center.

In one embodiment, the three-dimensional representation is tubular inappearance, but with flat sides of a predetermined color (for example,green), the sides equal to the number of parameters in the group whenthe values are normal. The respective walls of the tube bow in or out ifone or more of the parameters deviate from the normal range and thewalls turn a different color (for example, yellow) and then yet anothercolor (for example, red) when the values fall or rise to thresholdlevels. A bi-directional arrow may be shown across the tube between twoparameters when a relational breach or pathophysiologic divergence isidentified. The system may be adapted to provide the healthcare workerwith the option to move through the three-dimensional representationalong the time axis with each sequential cross-section appearing on thedisplay in a manner similar to the scrolling through CT scan imagesprovided to heath care workers today. For example, with each usergesture, the user requests advancement along the time axis (thelongitudinal axis of the tube) to the next sequential cross-sectionimage or layer. The two-dimensional image represents a plurality of datapoints of different parameters in a predefined spatial relationship thatvisually and simultaneously displays both the relationship to otherparameters and the relationship to normal values with eachcross-sectional image. This provides immediate visual cues to thehealthcare worker of the relational state of the parameters to normalityand to other parameters. Upon scrolling the timing of theserelationships and detected deviations form normality (which may berelational) are dynamically evident with rapid iteration, which can beautomated providing a pseudo animation of the three-dimensionalrepresentation (as by scrolling back and forth). Examples of parametersthat can be included within the representation include the parameterspreviously discussed as well as body dimension characteristics (forexample, weight), temperature, laboratory values (such as hemoglobin orwhite blood cell count), and financial (or resource) expenditure to namea few. In another embodiment the viewer emulates the instant or a seriesof prior medical monitor representations (such as, for example anoximeter or ventilator). This allows a physician, for example, to viewthe patient's medical records in a remote location through ubiquitousstandard secure internet protocols to see a simulation of the monitor,which would otherwise be visible only at the bedside. The viewer mayalso provide a reanimation of the patients signals as will be discussed.Different options for viewing the signals in these and other ways may beselectable by the user, as by a pull down window, so that the user canchoose to render and view the various time series in his or herpreferred viewing mode.

Another exemplary embodiment of the invention may be adapted to assistin the classification of the type of a patient's sleep-disorderedbreathing by iterating though the patterns outputted and or responses ofthe patterns to therapy to define the best match. For the purpose ofillustration, an exemplary embodiment of the present invention may beadapted to prioritize and weight a plurality of characteristicsassociated with a condition of interest such as sleep-disorderedbreathing. Parameter patterns associated with the plurality ofcharacteristics and/or appropriate treatment responses may becharacterized along at least one time series. An iterative operation maybe performed on the characteristics to compare the patterns andtreatment responses (such as a prolonged fall in SPO2 terminated by acluster) to each type grouping. The best matching type may be identifiedand an output of an indication based on the best matching type provided.The treatment of a patient may be adjusted based on the identificationof the best matching type.

By way of example, the patterns of sleep-disordered breathing may bedivided into five primary types, each with a grouping of differentcharacteristics. In one embodiment, the processor is programmed todetect at least one of the characteristics below and to take at leastone of the following actions based on the detection of thecharacteristic or combination of characteristics: output an indicationof the characteristic, output an indication of the most likely type orcombination of types of sleep disordered breathing, output an indicationof the therapy most likely to be effective, automatically adjusttreatment to the therapy most likely to be effective, provide a gradedauditory or visual alarm. The five types of sleep disordered breathingare:

Type I. Upper Airway Instability—Obstructive Sleep Apnea, characterizedby:

-   -   1. Paroxysmal reentry derived clusters often with precipitous        onset and termination.    -   2. Variable length of paroxysmal clusters.    -   3. Paroxysmal clusters are commonly separated by intervals of        stable time having variable lengths.    -   4. Variable amplitude of reciprocations (SPO2 often falls below        about 85% or less).    -   5. Very precipitous reciprocation recoveries.    -   6. SPO2 “fall to rise” slope ratio commonly less than about 1.    -   7. Elimination of clusters is generally complete with CPAP        alone.    -   8. Complete reciprocations even during REM (fall to rise        amplitude ratio of greater than 0.8 and usually about 1) and/or        the peak of the recovery exceeding 85-90.    -   9. Severity of clusters and amplitude of reciprocations increase        during REM.    -   10. Severity of clusters and amplitude of reciprocations        increase with various body positions.    -   11. Periods of prolonged slow reciprocations with brisk        recoveries occasionally occur between typical rapidly cycling        clusters (as due to obstructive hypoventilation).    -   12. Cluster Pattern is often defined by regular reciprocations        but reciprocations also can be irregularly irregular.    -   13. Nasal oxygen alone reduces but generally fails to eliminate        moderate or severe SPO2 clusters.    -   14. Nasal oxygen alone fails to improve (and may increase) the        severity of airflow clusters.    -   15. Low or normal Baseline awake SPO2.

Type II. Hypo-sensitivity/Hypo-responsive Induced VentilationInstability (Primary hypoventilation represents a rare pure form of thisprocess), characterized by:

-   -   1. Cyclic Periods of prolonged reciprocations with slow declines        (greater than 3 minutes but often 10-20 minutes or more) with        brisk recoveries may occur generally without rapidly cycling        clusters.    -   2. Spontaneous precipitous recovery and/or precipitous recovery        upon the occurrence of a stimulus to the patient such as an        auditory alarm.    -   3. Increased SPO2 and or CO2 wake-sleep gradient.    -   4. Incomplete reciprocations (for example with an amplitude        ratios of less than about 0.8 and/or a peak of the recoveries        being less than 85-90) are common, especially during REM.    -   5. Variable (often high) amplitude of reciprocations (SPO2 often        falls below 85% during reciprocation).    -   6. Precipitous recoveries (but sometimes incomplete).    -   7. Amplitude of desaturation component of reciprocations often        increases with REM.    -   8. Elimination of prolonged reciprocations is often incomplete        with even high levels of CPAP (indicates a central component).    -   9. Complete elimination of SPO2 clusters and low SPO2 often        achieved with BIPAP but often requires an IPAP-EPAP difference        of greater than 4, full face mask, and backup rate.    -   10. SPO2 “fall to rise” slope ratio less than 1 and generally        very low such as for example 0.1 or 0.2 or less.    -   11. Sustained falls in SPO2 often responds completely to low        flow oxygen alone but the falls in airflow or other ventilation        signals may be unaffected or worsened by oxygen alone.    -   12. Low or normal Baseline awake SPO2.

Type III PolymorphicIII (Complicated) Sleep Apnea (generally acombination of type I one and type II), characterized by:

-   -   1. Incomplete reciprocations (for example with amplitude ratios        of about 0.8 or less and or recovery peaks of leas than about        90-85 or less)) are common, especially during REM.    -   2. Periods of prolonged reciprocations with slow declines        (greater than 3 minutes but often 10-20 minutes or more) with        brisk recoveries may occur between rapidly cycling clusters.    -   3. A plot of the amplitude of the peaks can look similar to a        plot of a Type II patient indicating progressively weaker        functional arousal (physiologic recovery) response followed by        improvement in the functional arousal (physiologic recovery)        response.    -   4. Increased SPO2 wake-sleep gradient (high delta).    -   5. Highly unstable nadirs and_unstable peaks.    -   6. Clusters often begin after prior progressive (often slow)        fall in SPO2 of 4-8% or more.    -   7. Variable (often high) amplitude of reciprocations (SPO2 often        falls below 85% during reciprocation).    -   8. Precipitous Recoveries (but sometimes incomplete).    -   9. Cluster pattern is often regular but also can be irregularly        irregular.    -   10. SPO2 “fall to rise” slope ratio less than 1 is most common.    -   11. Low or normal baseline awake SPO2 (low 90s or high 80s is        typical).

Type IV Delayed Response Induced Ventilation Instability (e.g. CheyenneStokes Respiration), characterized by:

-   -   1. Monotonous “waxing and waning” pattern often with slow and/or        prolonged reciprocation recoveries.    -   2. Regularly, regular pattern with monotonous (often about 40-70        second) cycle length for most of the night with 15-30 minute        periods of stability due to REM sparing.    -   3. Clusters often begin with the onset of sleep without prior        fall in SPO2.    -   4. SPO2 “fall to rise” slope ratio often near about 1 (but also        commonly less than 1).    -   5. Partial reduction in reciprocation amplitude may occur with        CPAP.    -   6. Increase in reciprocation amplitude with BiPAP.    -   7. REM sparing.    -   8. Both Airflow and SPO2 Cluster are often very responsive to        nasal oxygen alone.

Type V Hyper-sensitivity/Hyper-responsive Induced VentilationInstability, characterized by:

-   -   1. Clusters begin with the onset of sleep without prior fall in        SPO2.    -   2. Clusters of central apneas often with obstruction at end of        the apneas low amplitude of reciprocations (desaturation often        fails to fall below about 90%).    -   3. Precipitous recoveries of reciprocations.    -   4. Clusters more severe with high CPAP or BiPAP.    -   5. REM sparing may occur.    -   6. Often high awake baseline SPO2.    -   7. SPO2 clusters (but not airflow clusters) are often eliminated        by low flow oxygen.

Additional categories of breathing may be employed if desired, such as:Type VI. Non-Hypoventilation induced Hypoxemia, characterized by:

-   -   1. SPO2 fall is associated with at rise in minute ventilation        (pathophysiologic divergence)    -   2. Sustained (greater than 3 minute and often 20-60 minute or        more) falls in SPO2    -   3. Slow SPO2 recoveries    -   4. Often responds completely to oxygen but may require high flow        (46 liters per minute)    -   5. Often fails to respond to CPAP or BiPaP

Considerable overlap between the types of sleep disordered breathingcommonly occurs.

In another exemplary embodiment, the system detects a cluster pattern oftiming delay or of spatial variation between similar physiologic eventsmeasured at different sites on the body. Such a cluster patterns isinduced, for example, by a rise in sympathetic tone which reduces thediameter of the tiny blood vessels in the fingers thereby causing adelay or slope attenuation at a second sensing site in comparison with afirst sensing site wherein the second site receives blood flow fromvessels which are narrower or longer than at a first site. In anexample, the method can include the application of a first pulseoximetry probe to a first site and a second pulse oximetry probe to asecond site the second site being more remote form the heart than thefirst site. The first site can, for example the thumb, ear, or foreheadand the second site can be, for example the third digit or anotherdigit. For example a combination of the thumb and third digit may beused since the middle finger generally has considerably longer smallvessel supply through the palmar arch and the digit itself.

A time series of the plethesmographic pulse is derived from both sitesin parallel and a cluster pattern of variance between matchingcharacteristics of the plethesmographic pulse (for example thecharacteristics noted in the previous listing) is defined. For example,a time series of the delay between the upstroke onset, and/or peak ofthe pulse waveform at the second site in comparison with the first site.In another example, a time series is provided comprised of thedifference in slope of pulse waveform at the second site and the firstsite (for example, of the difference in slope of the upstroke at eachsite). These relational time series can then be analyzed to monitorsympathetic tone, which rises for example with drug infusion orhemorrhage and for detection of clusters to indicate the presence ofclusters of variations in sympathetic tone or the presence of sleepapnea or other disease processes.

The time series of delay can also be rendered for time series ofdifferent parameters, for example the delay between matchingcharacteristics of the arterial pulse waveform and the pleth waveform orthe onset of the QRS and the onset of the rise of the pleth waveform. Inone embodiment, a single oximeter is used with the output triggeringlight delivery split or with rapidly alternating light delivery to eachprobe to allow the pleth to be derived from each site with a singlepulse oximetry unit. In another embodiment, a plurality of oximeters areused and each time series is analyzed in parallel to derive the timeseries of the delay or variation difference or to otherwise track thedelay or variation difference.

In one exemplary embodiment, a system especially useful for homedetection of sleep apnea comprises or includes a very small, low power,battery-operated pulse oximeter, which may be patient mountable to thehand, wrist, forearm, upper arm, head adjacent the ear, nose, forehead,around the neck or the like. The oximeter preferably includes atransmitter, or has sufficient memory for storage of at least 7-8 hoursof a time series of high fidelity plethesmographic pulse waveforms alongwith a high fidelity time series of the SPO2 with or without a timeseries of the heart rate. The SPO2 data is desirably recorded ortransmitted with a sampling rate of about 0.5-1 hertz, although a sloweror faster sampling rate can be used depending on system designconsiderations. As an example, the plethesmographic pulse could berecorded or transmitted with a sampling rate of 25 hertz or higher witha transmission of data in packets to a receiver updating every fiveseconds to save power. The pluralities of stored time series recordingsmay then be downloaded into an analysis program for analysis and viewingusing, for example, a modem or a direct connection. The plurality oftime series are then analyzed for clusters for the detection andcharacterization of sleep disordered breathing. In one embodiment, thisoximeter can be connectable (for example removably dockable orintegrally combined with) a pressure or flow sensing device (as may bedeployed with a nasal cannula and/or with a positive pressure deliverydevice). The system may desirably include sufficient memory to recordparallel high fidelity time-series of airflow to produce an additionalparallel time series for downloading with the time series grouping fromthe oximeter. One exemplary method includes detecting pathophysiologicoccurrences that may represent abnormal patient conditions. Anotherexemplary method may comprise generating a plethesmographic output,generating an SPO2 output, programming a processor to compare the atleast one component of plethesmographic output to at least one componentof the SPO2 output to detect a pathophysiologic occurrence or the like.

The component of both the plethesmographic pulse and SPO2 compared caninclude, for example, the patterns or frequency of the variation ofamplitude, slope of the upstroke, area under the curve, cycling, timing,or rate to name a few and these can be compared for example using theprocessing methods discussed supra or by another method.

In another exemplary embodiment, a nasal cannula (such as a dual lumennasal cannula capable of both delivering oxygen and sensing airflow,pressure waveforms and or CO2 waveforms) is deployed in connection witha processor for storing a time series of airflow, pressure waveforms andor CO2 preferably with a parallel time series indicative of the flowrate of oxygen delivered to or flowing to the cannula. The time seriesof parameters derived from this multi-function cannula is then analyzedfor clusters indicative of ventilation instability in relation to oxygenflow (or to identify disconnect of the nasal cannula from the nose). Theanalysis can be provided in real-time and can, for example, be triggeredby automatically by a patient-related action or physiologic output as byan actigraph or body position indicator or manually by the patient.

Another exemplary embodiment for relational processing comprises amechanical ventilator with integrated circulatory monitoring. In thisembodiment, at least one time series of at least one parameter derivedfrom a mechanical ventilator such as the 840 Ventilator manufactured byNellcor Puritan Bennett, (and which can also include a positive pressuredelivery device such as a CPAP unit or Bilevel ventilator) is analyzedin combination with a parallel time series derived from the flow ofblood in at least one blood vessel. For example, a time series of theminute ventilation and/or the inspiration to expiration (I:E) ratio canbe monitored in combination with a time series of the blood pressurefrom an invasive or noninvasive monitor, heart rate, cardiac output oranother circulatory parameter. The processor can then output therelationship between pattern of the circulatory parameter and patternsof variations of the parameter derived from the mechanical ventilator.In one embodiment, the mechanical ventilator is configured to receivesignal indicative of at least one circulatory parameter and arelationship between circulatory parameters and the ventilationparameters are displayed on the operator facing ventilator display aswell as the changes in circulatory parameters in association withchanges in ventilator output. For example, this display can be of thetypes discussed previously for the signal viewer embedded in thepatient's medical record.

For example, parallel time series of minute ventilation and heart rate(as for example derived from an oximeter that functions as a circulatorymonitor), pulse amplitude, and or blood pressure to name a few, may bedisplayed on the ventilator screen to assist the operator in definingventilator parameter settings, which are optimal both from a circulatoryand respiratory perspective. In one embodiment, the ventilator isconfigured to trigger or to queue the manual triggering of a measurementof blood pressure prior to initiating a change in ventilator settingsand then again within a short period of time after the change. Adifference in the blood pressure is identified on the ventilatordisplay. When the ventilation indexing oximeter is included as part ofthe mechanical ventilator the detection of SPO2, minute ventilationdivergence can be derived along with the detection of alterations incirculation in response to changes in the ventilator settings. Inanother embodiment, the variation in amplitude of the pulse pressure orvariation in amplitude of the plethesmographic pulse in association withthe inspiration and expiration cycle is determined and for exampleplotted as a time series of the variation. In another example, theamplitude of the plethesmographic pulse can be plotted in parallel withvolume time curve or pressure time curve or another ventilation timeseries on the display of the mechanical ventilator. The operatortherefore has immediate visualization of the effect of any change inventilation (or change in intravascular volume) on a plethesmographicpulse parameter (such as the amplitude, area under the curve, or slopeof the upstroke to name a few). In one embodiment, the ventilator has aninput for a high fidelity plethesmographic pulse (for example, from apulse oximeter), a high fidelity pressure tracing from an arterial line,an input from a noninvasive blood pressure device, which if the deviceuses a compression cuff can include a high fidelity plethesmographictracing from the arm under the cuff during partial and/or completeinflation and the actual systolic and diastolic blood pressure readings.With this embodiment, parallel vascular pressures and pulmonarypressures are provided (preferably in real time) on the ventilatordisplay. Indices and differences derived of these values can also bepresented and chosen for example from a menu on the display. In anotherexemplary embodiment, outputs that relate to oxygen delivery such asintermittent or continuous cardiac output, mixed venous or centralvenous oxygen saturation, or the arterial to mixed venous saturationdifference are inputted to the ventilator and provided as analyzed timeseries on the ventilator display. In one embodiment, the processor isprogrammed to automatically detect a relationship of a vascular oroxygen delivery parameter in relation to a ventilation parameter (suchas a 30% variation in plethesmographic amplitude) in relation to thevariation in positive airway pressure associated with inspiration andexpiration.

Another exemplary embodiment for relational processing is comprised of aspirocapnometer. FIG. 18 is a block diagram of a spirocapnometer inaccordance with an exemplary embodiment of the present invention. Thespirocapnometer is generally referred to by the reference number 1800.The spirocapnometer 1800 comprises a flow tube 1802. The flow tube 1802receives a gas flow 1804 from the mouth of a patient. The patient's lipsprovide a seal around the end of the flow tube 1802. The flow tube 1802,which may comprise a disposable pneumotachometer, includes a pressuresensing port 1806. The pressure sensing port 1806 is connected to a tube1807, which delivers a portion of the gas flow 1804 to a spirocapnometer1816.

The flow tube 1802 may have a flange covered with a screen or filter1808. The flow tube 1802 may additionally comprise a pilot tube 1810that is adapted to deliver a portion of the gas flow 1804 to acapnometer 1824. The pilot tube 1810 may be clipped onto the flange ofthe flow tube 1802 by a clip 1812. A tip 1814 of the pilot tube 1810 mayface up, in, or out and can have a very small diameter lumen. Ifdesired, a flow detecting mask, as may be provided for example byincorporating a flow tube into the lower port of a soft plastic mask(the lower port of these masks is now conventionally used to attach amedication nebulizer) may be provided.

The spirocapnometer 1800 may comprise a housing 1828 that houses aplurality of instruments. The housing 1828 may house the spirocapnometer1816 and the capnometer 1824, as well as one or more of an oximeter1818, a processor 1820, a display device 1822 and a transmitter 1826(such as a telemetry transmitter). The housing 1828 may assume a widerange of configurations, including a handheld configuration or a fixedconfiguration, such as for positioning on a rolling stand.

The exemplary embodiment illustrated in FIG. 18 can provide continuousand spot assessment of respiratory status in the emergency room or onthe awards. A flow sensor that is preferably disposable (such as apneumotachometer of a spirometer) is connected with a capnometer toproduce a spiro-capnometer. This device provides for the derivation of atime series of exhaled CO2 along with the time series of flow during aforced exhalation maneuver, which can include an exhalation of theexpiratory reserve volume or vital capacity maneuver and desirably usinga simple disposable pneumotachometer. In one method the patient is askedto breathe normally and then at the end of a normal tidal exhalation isasked to exhale till complete emptying rendering an end forced exhaledCO2 (EFCOs) which is not preceded by a large inspiration (therebypreventing the dilution associated with the maximum inhalation). The CO2monitor can be a side-stream capnometer connected to an accessory porton the disposable pneumotachometer or other flow sensor or can be amainstream capnometer with light emitter and transmitter connectable toat least one window on or connected with the tube of a disposablepneumotachometer or other flow sensor. This system provides an exhaledCO2 waveform with the spirometry data collection and further enhancesthe value of the exhaled CO2 evaluation since the forced and/or slowvital capacity maneuvers are associated with near complete emptying ofthe lung (except for residual volume) so that a plateau of CO2 moreindicative of the PaCO2 is derived. The system thereby provides ameasurement of the “end vital capacity CO2” (EVCO2) rather than theconventional end tidal CO2 (ETCO2). Because of the dilution of the largepreceding tidal volume, the EVCO2 is not considered as useful as theEFCO2. In the absence of a high respiratory rate or shallow breathing ahigh EFCO2 to ETCO2 gradient is suggestive of airway obstruction. In oneembodiment, the processor 1820 is programmed to detect the plateau as byidentifying a minimum slope of CO2 rise during exhalation and/or thehighest CO2 value and report these values along with the forced vitalcapacity and exhaled CO2 waveform adjacent the volume time curve derivedof the FVC maneuver. For example, the reported EFCO2 can be the CO2identified when exhalation flow falls to about zero or the slope of theexhaled CO2 falls to about zero.

As illustrated in FIG. 18, the spirocapnometry system 1800 can beintegrated with the pulse oximeter 1818 to provide for othermeasurements such as a simultaneous SPO2 and the ventilation indexedoximetry value. The method can include determining the resting minuteventilation and the oxygen saturation value corresponding to the restingminute ventilation and then determining the exhaled carbon dioxide atthe end of a maximal exhalation maneuver (as for example performed afteror immediately at the end of resting minute ventilation measurement).The EFCO2 value can then be used to calculate the ventilation oximetryindex instead of the minute ventilation or both can be used. A methodfor directly comparing exhaled CO2 and SPO2 and for calculating aventilation adjusted and/or exhaled carbon dioxide adjusted oximetryvalue is discussed in U.S. Pat. No. 6,609,016 by the present inventor(which is incorporated herein by reference as if completely disclosedherein). When implemented with a spirometer, a capnometer and anoximeter, the system 1800 may be referred to as a SpiroCapnOximetry(SCO) system. The system 1800 can be combined in a single small compacthousing configured to be hand carried to the patient's bedside or can bemodular with the spirometer, capnometer and oximeter connectable (suchas dock able, by cable connection, or by transmission-receptionconnection) or connectable to a central processor (as, for example, bycabling or direct docking or by transmission-reception basedconnection). Alternatively, the SpiroCapnometer can be employed alonewithout the oximeter. Additional sidestream or mainstream gas samplingcan be added to detect and quantify, and characterize the patterns ofother exhaled gases such as nitric oxide, carbon monoxide, or oxygen toname a few.

When implemented as a spirocapnoximetry system, the system 1800 can becombined with other monitors such as an electrocardiogram and bloodpressure monitor as, for example, mounted on a rolling stand forimmediate bedside availability in the emergency room. The system isparticularly suitable for the evaluation of shortness of breath in theemergency room and on the wards providing a dyspnea evaluation montage,with which the patient can be followed by multiple spot checks, or on acontinuous basis. The montage displayed or exported by the processor1820 can include, for example, the FVC, FEV1, peak flow, end vitalcapacity CO2 (EVCO2), EFCO2, end tidal CO2 (ETCO2), the EFCO2-ETCO2difference, flow volume loop, resting SPO2 (RSPO2), hyperventilationSPO2 (HSPO2), the HSPO2−RSPO2 difference, the resting heart rate,resting respiratory rate, I:E ratio, the resting minute ventilation, theventilation indexed oximetry (VOI) value (calculated using the minuteventilation to estimate the paCO2 or by using the measured EFCO2 as asurrogate value for the PaCO2 applied in the formula) or the like. Anadjustment of 2-3 mm may be added to the EFC02 or another adjustment maybe made by the processor for example if the exhaled CO2 fails to reachplateau at the end of the forced exhalation maneuver. Examples of otherdata that may be exported include the inspiration to expirationvariation of plethesmographic pulse (the variation can be the amplitude,slope, or area under the curve or slope etc.) to name a few.

One exemplary method using the oximeter 1818 for detectingpathophysiologic occurrences abnormal patient comprises generating aplethesmographic output comprised of a time series indicative of thewaveform of a plurality of the patient's pulse, generating acapnographic output comprised of a time series indicative of thewaveform of a plurality of the patients tidal breaths, programming theprocessor 1820 to compare the plethesmographic output with thecapnographic output to detect a pathophysiologic occurrence. All ofthese can be generated and displayed along with a programmedinterpretation within a few minutes with a simple bedside resting minuteventilation measurement and subsequent FVC maneuver. In one embodiment,these values and the associated time series are uploaded automaticallyto the patient's medical records to subsequently become part of the timeseries viewer discussed supra. The time series of these values can thenanalyzed and viewed in the central patient record along with the timeseries of other monitored parameters. An application program may beadapted to analyze and view physiologic datasets as accessible from aprimary program used to view the patient's electronic medical records. Alink may be embedded in a user interface of the primary program toinduce loading and operation of the application program. The applicationprogram, when launched, may acquire at least one time series ofphysiologic data and transmit the time series to a central processor forstorage in a database. The application program may be linked to thedatabase. When the link is activated (e.g. clicked), the relevantdataset may be loaded, analyzed and displayed.

In one exemplary embodiment, the patient monitor system 1800 may beadapted to evaluate a patient using the display 1822. The spirometer1816 may be attached to the flow tube 1802 for measuring, at least,exhaled flow and producing at least a first output indicative of thevital capacity. The capnometer 1824 may additionally be connected to theflow tube 1802. The system 1800 may be adapted to produce a secondoutput indicative of the patient's exhaled carbon dioxide. The processor1820 may be programmed to determine an indication of the exhaled carbondioxide value, which relates to the vital capacity. The monitor 1800 canfurther including the processor 1820 being programmed to receive aninput indicative of a body dimension of the patient such as the height,weight, body surface area and/or the age and sex of the patient. Theoximeter 1818 (if included in the system 1800) may be adapted todetermine an oxygen saturation value for connection with the patient,the processor 1820 being programmed to compare the carbon dioxide value,which relates to the vital capacity to the oxygen saturation value.

In another embodiment, the flow tube 1802 defines a resistance to flowor otherwise has an obstruction such as a spinning member. The flow tube1802 may employ ultrasonic or other methods for determining flow thoughthe tube, the tube having a first end for receiving exhaled flow from apatient and a second end for venting the exhaled flow. The spirometer1816 or other flow meter may be connectable with the flow tube 1802, thespirometer 1816 being programmed to receive an input derived from ameasure responsive to the resistance or to the other parameters used todetermine flow through the flow tube 1802. In this manner, the system1800 may determine the flow rate through the flow tube 1802 using theinput and to determine the vital capacity of the patient. A carbondioxide monitor (such as the capnometer 1824) may be connectable withthe flow tube 1802 for monitoring the carbon dioxide flowing through theflow tube 1802. The processor 1820 may be programmed to determine anindication of the time series of exhaled carbon dioxide and to determinean indication of the exhaled carbon value, which relates to the end offorced exhalation or to the vital capacity. At least a portion of theflow tube 1802 can be disposable or, preferably, the entire flow tube1802 can be disposable. The flow tube 1802 can be marked and/or coded(for example, with a bar code 1828 or another method) with a calibrationvalue, which for example can relate to the resistance to flow throughthe flow tube 1802. For example, the flow tube 1802 can be of thedisposable type marketed by Nellcor Puritan Bennett for use theRenaissance II Spirometer. A small side port can be provided along theflow tube 1802 for attachment of (for example) a cannula of amicro-stream capnometer of the type marketed by Oridian. Alternatively,to avoid the need to modify the presently manufactured flow tube, thesmall pilot tube 1810 (illustrated in FIG. 18) or cannula may beattached as by adhesive between the screen and the housing such that thelumen of the gas sampling pilot tube 1810 or cannula projects adjacentthe flow stream at the distal end of the flow tube. In one embodiment,the gas sampling pilot tube 1810 includes the clip 1812 for clippingonto the flow tube 1802 adjacent the distal end with the end of thepilot tube 1810 projecting adjacent the exhaled flow stream emitted fromthe flow tube 1802. In one embodiment, the clip 1812 comprises a pilottube holding clip into which the pilot tube 1810 is fixedly bonded orotherwise snapped and which holds the projecting pilot tube 1810 suchthat it projects about 1-10 millimeters or more from the distal end ofthe flow tube 1802 so that the end of exhalation is detected by thecapnometer 1824 even if the patient does not inhale. The pilot tube 1802can be laterally shielded to prevent dilution of the exhaled gas streamby ambient airflow. In another embodiment, the pilot tube is connectedto the flow tube 1802 along the length of the tube so that the lumen ofthe pilot tube is positioned adjacent to or within the lumen of the flowtube. In this embodiment, the onset of inhalation is detected by thewashout of CO2. When a nasal cannula is applied and connected with thecapnometer instead of a flow tube, the patient can be periodically asked(or the monitor can be programmed to ask the patient) to exhale toresidual volume through the nose with the mouth closed and these peakCO2 values resulting for the forced exhalation can be used to calculateVentilation indexing SPO2 values as by the previously discussedformulas.

The present invention provides method for evaluating a patient,comprising disposing a flow tube of a flow meter adjacent a patient andmeasuring using the flow meter, the exhaled flow through the flow tubeto determine at least a first output indicative of the vital capacity;disposing a capnometer in connection with at least a portion of the flowtube, the capnometer being capable of producing a second outputindicative of the carbon dioxide exhaled through the flow tube and usinga processor, determining an indication of the exhaled carbon dioxidevalue which relates to the vital capacity.

If the capnometer is combined with a mechanical ventilator as byproviding an capnometry input into the mechanical ventilator, theventilator can be programmed so that the operator can select manual orautomatic (as every 15 minutes for example) the determination of the endforced exhalation CO2 as will be described. Upon manual or automaticqueue, the processor 1820 delays the next breath until exhalation flowfalls to about zero or the slope of the exhaled CO2 falls to about zerothrough a forced exhalation of the patient in the manual mode (EFCO2)and at passive end of a prolonged exhalation in the manual or automaticmode. The measurement of the CO2 (such as the highest CO2) is identifiedat either of these points. In one embodiment, the ventilator isprogrammed to allow passive exhalation to functional residual capacity.

In another embodiment a nebulizer or other medication generating deviceis provided connected with the flow sensor tube (as for use with adisposable vane spirometer or the like) so that the patient can inhalemedication while the minute ventilation and the patterns of tidalbreathing such as the inspiratory to expiratory time or slope ratio iscontinuously monitored. The nebulizer of such a system can be driven byan oxygen cannula and the processor can adjust the calculated minuteventilation for the added flow rate of oxygen automatically or this canbe accomplished manually. This is particularly suitable for continuousdelivery of albuterol or other inhaled bronchodilator.

In another embodiment, the capnometer 1824 may be used in combinationwith the oximeter 1818 to assess the relationship between ventilation,SPO2, and the plethesmograph output of the oximeter (such as thephotoplethesmograph). The capnograph can be employed to determine therespiration rate and this can be compared with the SPO2 to identifypathophysiologic divergence between the SPO2 and the respiration rate orthe end tidal CO2 (ETCO2) as previously discussed. For example, afalling ETCO2 coupled with a falling SPO2 comprises pathophysiologicdivergence of the ETCO2 and SPO2. Furthermore, a rising respiratory rate(as derived for example from the capnograph) coupled with a falling SPO2or a rising CO2 comprises pathophysiologic divergence. The system 1800can include a side-stream capnometer or mainstream capnometer.

According to one aspect of the present invention, the time-series of theprimary capnographic breath by breath output is compared with a timeseries of the plethesmograph, a relationship between a change in atleast one component of the plethesmographic pulse (such as a fall inamplitude, fall in the area under the curve, or slope of the upstroke,to name a few) with a portion of the inspiration-expiration curve of thecapnograph can be used to asses the influence of time series of CO2, atime series of nasal pressure can be compared to provide theinspiration-expiration curve for comparison. In an example, the percentvariation of the plethesmographic pulse amplitude or area under thecurve (AUC) during or at the end of inspiration and the plethesmographicpulse amplitude or AUC during or at the end of expiration.

According to one embodiment of the invention, the tidal nasal pressureand tidal CO2 can be monitored using the same cannula as for example byattaching a pressure transducer to the nasal tubing used for collectingthe CO2 sample. This can be achieved for example by providing anaccessory tubing bifurcation adjacent the nasal prongs or distallyadjacent the capnometer, which leads to the pressure transducer.Alternatively, the pressure transducer can be built into the capnometerso that the monitor provides both outputs. In one embodiment, the outputfrom the capnometer is provided with a contemporaneous output of thenasal pressure or other nasal flow sensor and at least one component ofthe time-series of the nasal pressure or flow sensor is analyzed andcompared with the capnographic breath by breath time series to compare achange or pattern of change in the nasal pressure or flow sensor (suchas a rise or fall in amplitude, a rise or fall in the area under thecurve, or change in the slope of the upstroke, to name a few) with achange or pattern of change in the capnograph (such as the amplitudepeak value, a rise or fall in amplitude, a rise or fall in the areaunder the curve, or a change in the slope of the upstroke, to name afew).

In another embodiment, a method for monitoring sleep disorderedbreathing comprises a disposing a nasal, oral, and/or nasal oral cannula(for example of the type marketed by Oridian for delivery of oxygenduring monitoring of end tidal) in simultaneous connection with apressure transducer and a side stream CO2 sensor (for example,connecting the nasal pressure monitor to the portion of the tubing whichis positioned at the neck or at the proximal end of or otherwise alongthe oxygen delivery portion of the tubing. The tubing connected to thetransducer can be accessory tubing connected to the main oxygen deliverylumen or can be the main oxygen delivery tubing itself. The nasalpressure is then monitored along with a contemporaneous measurement ofcarbon dioxide. The processor 1820 can be programmed to detect a fall inthe amplitude of tidal nasal pressure coupled with a rise in amplitudeof the tidal carbon dioxide value and to output an indication such as“evidence of reduced drive or obstructive hypoventilation” if the nasalpressure has an obstructive shape this can be so indicated. In thealternative, if the processor 1820 detects a rise in the amplitude ofthe tidal nasal pressure coupled with a rise in amplitude of the tidalcarbon dioxide the processor can output an indication ofpathophysiologic divergence of nasal pressure and CO2. The developmentof a rising CO2 and rising tidal nasal pressure suggests a latedeclining respiratory state and portends an adverse outcome withouttimely intervention. The cluster patterns of nasal pressure and CO2 (asdiscussed previously) can likewise be detected using the combinedpressure-CO2 monitoring described above.

In another embodiment, a direct or indirect capnometer is coupled withthe flow tube connected with a patient during cardiopulmonaryresuscitation. According to an exemplary embodiment of the presentinvention, a plurality of time series derived of the capnometer isoutputted and analyzed by the processor as by the methods discussedpreviously. The time-series of the maximum CO2 per breath (as forexample the ETCO2) is compared with the slope of plateau of the CO2 perbreath. If the ETCO2 is falling at the same time the slope is rising orif the slope exceeds about zero and the CO2 is low (such as less thanabout 12-15), then the processor may produce an indication so that theoperator is warned that the CO2 value may be low due to a high minuteventilation or an excessive respiratory rate during CPR. This canprevent the erroneous use of a low ETCO2 due to excessive ventilation asan indication to terminate CPR efforts. In addition, the processor-baseddetection of a slope exceeding about zero can trigger an indication thatthe breathing rate should be slowed. In one embodiment, the processor1820 may be programmed to provide an indication (such as a flash, avibration or a sound, to name a few) when there is no longer anyobjective evidence that the patient is passively exhaling (indicatingthat exhalation is complete) so that the operator has objective evidence(such as a positive CO2 slope or continued exhaled gas flow at a flowsensor) of ongoing exhalation or the end of exhalation so that theoperator can consider delaying the administration of the next breathuntil the patient has completely exhaled to reduce the adverse potentialfor auto-PEEP. In an automated system for delivering breaths during CPR,the processor 1820 may be programmed to determine the end of exhalationand so that the operator can obtain a more accurate reflection of thetrue state of circulation using the end expiration CO2 monitor and sothat auto-PEEP is minimized. In this respect, the system 1800 providesfor the detection of the exhaled CO2 value at the end for passiveexhalation (at functional residual capacity) which value should be lessaffected by breathing rate and tidal volume. The potential forauto-PEEP, which can severely compromise circulation and lower the ETCO2values during CPR is discussed in more detail in U.S. patent applicationSer. No. 10/080,387, entitled “Asthma Resuscitation System and Method,”filed Feb. 25, 2002 and assigned to the present inventor (the contentsof which is incorporated herein by reference as if completely disclosedherein).

In one exemplary embodiment, the monitor is configured to detectfunctional arousal threshold failure. An example of a method for usingan oximeter for detecting arousal threshold failure includes generatingat least one time series of a at least one physiologic parameter,detecting at least one reciprocation having a threshold value, definingthe threshold value, determining the presence of arousal thresholdfailure based on the value. The threshold value can be a duration orlength, such as an apnea or a desaturation duration, a nadir (such as adesaturation nadir), or a peak (such as an exhaled carbon dioxide peak).

Another exemplary embodiment includes generating at least one timeseries of at least one physiologic parameter, detecting at least a firstreciprocation having a first threshold value (or a value indicative of,or resulting from of the first threshold value), defining a valueassociated with the first threshold value, determining at least a secondreciprocation having a second threshold value, (or a value indicativeof, or resulting from the second threshold value) determining thepresence of arousal threshold failure based on the relationship betweenthe first threshold value at the second threshold value. Therelationship of the plurality of threshold values can be, for example amagnitude difference between absolute values of the nadirs, a magnitudedifference between absolute values of the decline events, a slope ofconsecutive nadirs, a duration of a nadir range, a frequency or range ofpatterns of nadir values, to name a few. In still another exemplaryembodiment, a plurality of threshold values are determined and abaseline mathematical relationship, such as an average range, of thethreshold value is identified. The presence of functional arousalthreshold failure (or recovery threshold failure) is detected upon theidentification of a threshold value, which is sufficiently lower (forexample 10% lower), higher or longer than the threshold baseline.Alternatively, the presence of threshold failure can be detected uponthe identification of a value indicative of the minimum threshold value(such a nadir SPO2 value of about 65-75).

FIG. 19 is a graph showing an example of functional arousal thresholdfailure, which may be detected and analyzed in accordance with anexemplary embodiment of the present invention. The graph, which isgenerally referred to by the reference number 1900, shows a 1000 secondtime series of SPO2 values along an x-axis 1902. A y-axis 1904 shows apercentage SPO2 value. A waveform 1906 shows a cluster pattern typicalof obstructive sleep apnea. Note that 11 consecutive reciprocations areshown with baseline average decline length of about 35 seconds and abaseline average nadir SPO2 value of about 80. The 12th reciprocationhas a decline length of about 90 seconds and a nadir SPO2 value of about55%.

An exemplary embodiment of the present invention may include a processorprogrammed to identify the 12th reciprocation (or the decline componentof the 12th reciprocation) as indicative of arousal threshold failureand to output an indication based on the identification. In anotherexemplary embodiment of the present invention, the monitor is configuredto detect functional arousal response failure (or recovery responsefailure). This type of failure can be due to absolute arousal responsefailure (wherein the arousal response is weak or absent) or relativearousal response failure (wherein the arousal response is weak relativeto the magnitude of the perturbation which triggered the arousal). Anexample of a method for using an oximeter for detecting arousal responsefailure in accordance with an exemplary embodiment of the presentinvention includes generating at least one time series of at least onephysiologic parameter, detecting at least one reciprocation having aresponse value, defining the response value, determining the presence ofarousal response failure based on the value. The response value can befor example, a magnitude or a relationship between a magnitude andanother value (such as a relationship between the fall magnitude and arise magnitude), a duration, a length, an area or another such value.For example a response value may be the airflow recovery or resaturationduration, a peak value (such as a resaturation peak value or arelationship between a peak and another value), or a nadir or arelationship between a peak and another value (such as a transcutaneouscarbon dioxide nadir) or an area, such as the recovery area (area underthe curve from the onset of resaturation to the onset of the next fall).In an example readily applied to, for example an SPO2 time series, theprocessor can be programmed to detect a threshold value indicative ofthe peak value and/or a magnitude of rise event and to provide anindication, for example to highlight the region of recovery failure andto output a text warning indicative of “Severe Recovery Failure” alongwith the instability index value which is calculated for the recoveryfailure and the surrounding associated patterns. The indication of thetype of adverse pattern for example “Unstable Hypoventilation Type III”can also be outputted. The instrument, such as a pulse oximeter can beprogrammed to provides the function of automatically detecting theinstability pattern type, the severity, and a range of failure modes,and to take action such as to provide an indication the instabilitypattern type, the severity, and a range of failure modes and or tocontrol therapy to treat the instability or reduce medication which maybe inducing the instability or provide medication which may reduce theinstability. For example, a patient controlled analgesia pump may have aport for connection with or connected with a reservoir containing anarcotic reversing agent and for automatically injecting or warning thenurse to consider injecting the reversing agent upon the identificationof a profoundly adverse pattern. The PCA device might for examplerequire the mounting of a vial of a reversing agent or an indicationthat such an agent is mounted before the device can be activated todeliver IV analgesia.

Another exemplary embodiment of the present invention includesgenerating at least one time series of at least one physiologicparameter, detecting at least a first reciprocation having a firstresponse value, defining a value associated with the first responsevalue, determining at least a second reciprocation having a secondresponse value, determining the presence of arousal response failurebased on the relationship between the first response value at the secondresponse value. In one exemplary embodiment, a plurality of responsevalues are determined and a baseline mathematical relationship, such asan average range, of the response value is identified. The presence ofresponse arousal failure is detected upon the identification of aresponse value, which is sufficiently lower (for example 10% lower),higher or longer than the response baseline. Alternatively, the presenceof response arousal failure is detected upon the identification of avalue indicative of the maximum response value (such a peak SPO2recovery value of about 92-84).

FIG. 19 also shows an example of arousal response failure. Note that 11consecutive reciprocations are present with baseline peaks of 94 andSPO2 fall-to-rise amplitude ratios of about one. The 12th reciprocationhas a peak of about 90 and then the 13th reciprocation has a peak ofabout 60. The processor, detecting this low peak, can output anindication in response to the low peak, which can, for example, be analarm indication that the patient has evidence of arousal responsefailure. A “low peak” which triggers an alarm may for example beprogrammatically fixed as peak below between about 90 and 80 or may beselectable from a menu. As discussed previously, the ratio of the SPO2fall magnitude to the subsequent rise magnitude may also be used totrigger the detection of arousal (recovery) response failure. Inaddition, the absolute value of the peak and/or the absolute magnitudeof the fall, and or the fall to rise magnitude ratio may be used incombination. For example, the processor may be programmed to require afall magnitude of at least 4%-6% or higher before any peak value ormagnitude ratio is considered indicative of response failure. In anotherexample the processor may be programmed to require a fall of at least4%-6% below a given threshold (such as 90%) before any peak value ormagnitude ratio is considered indicative of response failure.

In one exemplary embodiment of the present invention, a time series ofthe peaks (for example as the highest point of each rise object) isplotted and analyzed as by the aforementioned methods for falls and orreciprocations of the peaks. The detection of a significant relative orabsolute fall of the SPO2 peaks can be used to trigger an indication ofarousal response failure. In another exemplary embodiment, a time seriesof the nadirs (for example, as the lowest point of each fall object) isplotted and analyzed as by the aforementioned methods and falls and orreciprocations of the nadirs are analyzed and detected. The detection ofa significant relative or absolute fall of the nadirs can be used totrigger an indication of arousal response failure. Other parameters maybe used instead of SPO2 (or in combination with SPO2) to detect thesefailures.

Arousal response failure can also be detected by programming theprocessor to determine a measure of physiologic perturbation, resultingfrom an apnea and or hypopnea (examples include the magnitude of fall inoxygen saturation, the magnitude of rise in CO2, to name a few)determining a measure of response to the perturbation (examples include;the number of recovery breaths which follow the apnea or hypopnea, thechange in amplitude or duration of the EEG arousal of the EEG inresponse to the apnea to name a few). The processor can be programmed tocompare (for example, by calculating an index) the perturbation and theresponse. A time series of this index can then be analyzed eithermanually or automatically to detect a relative reduction in arousalresponse.

In another exemplary embodiment of the present invention, both theduration of the attenuation of the plethesmographic pulse amplitude andthe magnitude of the desaturation are used together to determineseverity. If clustered pulse amplitude attenuations are present andespecially if prolonged (for example more than 30 seconds) then thethreshold for outputting an indication of based on the detection of anassociated cluster of desaturations can be reduced. For example, if30-second amplitude attenuations are present, then an alert indication(such as an alarm) may be triggered by clusters of 4% desaturation,whereas if only 15-second amplitude attenuations are present, then thealert trigger threshold may be set at 8%. In this way the duration ofthe amplitude attenuations (which is a marker for apnea length) is usedas a marker of severity along with the magnitude of the desaturation toreduce the potential for oxygen to hide the severity of sleep disorderedbreathing when the magnitude of oxygen saturation is used alone. Thepattern, or a threshold measure of the plethesmographic pulse amplitudecan be compared with the pattern, or a threshold measure of the SPO2 (orratio of ratios) to detect a pathophysiologic occurrence. For example,the processor may be programmed to identify ventilation instability uponthe detection of three or more desaturations of greater than or equal to2% SPO2 coupled with a cluster of three or more plethesmographic pulseamplitude attenuations of 15 seconds or more wherein the recoverybetween the pulse attenuations is less then or equal to 120 seconds.

In one exemplary embodiment of the present invention, the processor isprogrammed to produce a different output in response to the detection ofa threshold breach and the detection of a cluster pattern. For example,the output in response to a threshold breach may be a continuousauditory alarm whereas the output in response to the detection of acluster (as by detection of a plurality of threshold breaches) may be atextual output or a discontinuous or periodic auditory alarm. Theprocessor can be programmed to produce a different alarm or signaloutput for each channel or for different patterns or differentseverities of the same pattern along the same channel.

In one exemplary embodiment of the present invention, the processor canbe further programmed to detect a plurality of threshold breaches ordirectional crossings within a time interval to detect a cluster. Thecrossings may be differentiated based on the direction of crossing. Forexample, a fall crossing may be detected and recorded as different thana rise crossing. In one exemplary embodiment, the number of crossings ofa threshold is determined for the fall and/or the rise and/or for acoupled fall and rise (which can be designated as comprising a singleset of crossings). A plurality of different threshold crossings can beset, for example providing a different threshold for the fall and therise and/or for a rise following a fall a specified period (such as atime period). In one embodiment, the various threshold crossings of theabove events or reciprocations are selectable from a menu. Differentthreshold levels can be set for different severity of clusters. Forexample, a plurality of crossings of 90% saturation may produce adifferent severity index and/or a different indication (such as analarm) than a plurality of crossings of 80% saturation. Further, thismay be different than a plurality of crossings of 75% saturation or aplurality of crossings of which a at least one of the crossings reach aselected threshold or which progress through thresholds of increasingseverity. In addition to providing a level for each crossing threshold,which may be different for fall than a rise, the severity may beadjusted based on the baseline of saturation prior to the fall or forthe absolute or relative magnitude of the fall or rise. The absence of athreshold rise crossing after a fall crossing wherein the rise crossingfails to occur within a period (as, for example, a period of time orafter a threshold magnitude of desaturation area such as a desaturationsecond product) may also be used to indicate severity. The relationshipsbetween the crossings (as for example the pattern of the crossings, thepatterns of the levels of crossings, and/or the time interval ordesaturation area between the crossings) can be used to trigger anindication such as an alarm. The relationships and patterns of falls,rises, and reciprocations, have been discussed extensively previously.Various exemplary embodiments employing simple or more complex analysisof threshold crossings to achieve a processor based analysis, result,and/or alarm of signal patterns similar or that achieved with morerobust pattern analysis (as disclosed, for example, in U.S. patentapplication Ser. No. 10/150,582) are included within the scope of thisteaching.

In another exemplary embodiment of the present invention, theinstability index may be adjusted for the occurrence of thresholdfailure or recovery (such as arousal) failure. In an example, thedetection of recovery failure may result in the increase in theinstability index by a multiple factor of two with the minimum valueafter the increase being raised to the minimum level selected for highseverity. Alternatively, a graded factor can be applied such that thedetection of a plurality of incomplete recoveries or the detection ofseverely reduced peaks or the detection of a prolonged period withincomplete recoveries can produce a greater instability index multipleor index adjustment value than the detection of less severe threshold orrecovery failures. The adjustment can be provided in real time so thatthe nurses are notified or drug infusion (such as a narcotic)automatically or manually reduced or stopped upon a threshold breach ora specific pattern of the severity index.

In one exemplary embodiment, a time series of the severity index, asadjusted for various precipitous adverse occurrences (such as recoveryor threshold failure), is analyzed (for example objectified as by thepreciously disclosed method methods) and the various alarms andindications are outputted based on the pattern of the instability indextime series. For example, the system may be programmed such that asudden sustained rise event may induce one output (one type ofindication or treatment adjustment whereas a reciprocation (risefollowed by a fall) may produce another. In this way, the pattern of thetime series of the severity instability.

In an exemplary embodiment for automatic treatment adjustment (such asdrug infusion or CPAP titration), the detection of an occurrence ofthreshold failure after upward titration of a medication or CPAP, as forexample after titration to CPAP levels above 15, can cause an actionsuch as an indication or an alarm or a modification such as a reductionin the therapy (such as the drug dose or level of CPAP). Thepreoperative detection of the occurrence of threshold failure orrecovery failure may be used to determine the operative or anesthesiarisk. In one exemplary embodiment of the present invention, a patient ismonitored preoperatively using a pulse oximeter for recovery and/orthreshold failure and then an adjustment in risk is assigned the patientbased on the detection of recovery and/or threshold failure. The patientcan be further monitored for recovery and/or threshold failurepostoperatively and therapy can be manually or automatically adjustedbased on the detection of recovery and/or threshold failure.

In an exemplary embodiment, precipitous state related threshold failureis differentiated from progressive or evolving threshold failure. Forexample, a state-related threshold failure is detected when a fall eventof a time-series of the SPO2 reciprocation nadirs declines precipitously(as with for example a slope of 5-15% per minute) and/or when a negativereciprocation of time series of the nadirs is sustained (for examplegreater than 3-5 minutes).

FIG. 20 is a graph showing an exemplary segment of a time series and anexpanded snapshot of a portion of data represented by the time seriessegment. The graph is generally referred to by the reference number2000. The segment of the time series is referred to by the referencenumber 2002 and the expanded portion of the time series is referred toby the reference number 2004. FIG. 21 is a graph showing an exemplarysegment of a time series and a different expanded snapshot of a portionof data represented by the time series segment. The graph is generallyreferred to by the reference number 2100. The segment of the time seriesis referred to by the reference number 2102 and the expanded portion ofthe time series is referred to by the reference number 2104.

In one exemplary embodiment of a system according to the presentinvention, the simulation of the real-time environment can beaccomplished through the storage and representation of a series ofsnapshots of data and analysis. A true representation of real-timeconditions of a monitored patient at a particular time (in the examplecalled a real-time point) can be derived by generating an analysis of asubset of a time series. In an example, the snap analysis accomplishesthis by extracting a contiguous set of points from a time series (suchas the time series segments 2002 or 2102) into a second time series(such as the expanded time series portions 2004 or 2104) and performingan analysis against this subset. This analysis executed as if the subsetof data is all of the data available, thus recreating real-timeconditions. This snap analysis can be performed against a single channelof data (e.g. oximetry) or against any number of related channels. Sincedata is stored with a timestamp (e.g. start time) the subset of data canbe extracted by describing a specific start and end time. The analysiscan be performed in the exact same way it is against a full night ofdata including the consideration of inter-channel properties.

In an exemplary embodiment of the present invention, three options existfor executing a snap analysis although more can be provided ifpreferred. In this example, the three options are: window,window-plus-thumbnail and past-omniscience. A window snap analysis canspecify both a start time and an end time that is other than the startand end time of the entire night of data. The past omniscient analysisspecifies an end time (or a given termination time) and assumes that theanalysis can see everything up to that point. Past omniscience willprovide for comprehensive analysis when given adequate resources fordata storage. All data up to the point specified (representing thereal-time point) is available for analysis.

The window options can be applied, for example, when data storageresources or processing power does not allow the analysis of all datasince the beginning of data collection. A start point can be indicatedas well, or a time span (e.g. 30 minutes) may be specified to indicatelimit the data that would be available in a limited-resource real-timeenvironment. The window-plus-thumbnail option specifies a start point(specifically or as an offset from the real-time point), but alsoprovides a data-structure containing information about data previous tothe start point that can be used to more accurately calculate valuesthat depend on information derived from an entire time series (e.g. areaabove the curve, number of clusters, or the like). The data stored inthe thumbnail may include index values as applied to previous dataand/or component values that would be used to create those indices suchthat data is differentiated sufficiently to calculate indices that applyto the entire time series previous to the real-time point with little orno loss of fidelity.

FIG. 22 is a graph showing a time series, along with a plurality ofthumbnails in accordance with an exemplary embodiment of the presentinvention. The graph is generally referred to by the reference number2200. A time series 2202 may be indicative of a physiological parameterof a patient. A plurality of thumbnails 2204, 2206 and 2208 may bederived from the time series 2202 to allow closer analysis of theunderlying time series data. The thumbnails 2204, 2206 and 2208 maycomprise XML (Extensible Markup Language) documents.

In one exemplary embodiment, each analysis is reduced to a set ofrelevant values stored in a single data structure—the analysisthumbnail. Each analysis object may have a separate thumbnail. Forexample, the thumbnails 2204, 2206 and 2208 may correspond to events,reciprocations and/or clusters or the like, each with relevant valuesstored. Higher-order thumbnails contain a collection of lower-orderthumbnails such that individual values can be iterated and/or aggregatedas necessary. For example, a cluster thumbnail contains a set ofreciprocation thumbnails. Further, a reciprocation thumbnail contains apair of event thumbnails. This hierarchical form of the data fits wellwithin an XML document.

Thumbnails allow for an abstraction of data for indexing, transmissionand efficient storage. The thumbnail can remain in memory, for example,without requiring storage for all data points in a time series. Thethumbnails of a high volume of cases can be reviewed with statisticalanalysis tools to identify correlations.

In one exemplary embodiment of the present invention, a snap analysis isprovided that renders an accurate simulation of the state of the patientfrom the perspective of a real-time point. A set of snap analysesprovides a simulation of the changes of that state over time. Theanalysis thumbnail allows for an abstract representation of the state ofthe patient from the perspective of a real-time point. A collection ofanalysis thumbnails provides a sampling of those representations overtime. Given a particular sample rate (e.g. about 0.5-10 minutes minutealthough other rates can be used), a series of snap analyses can bestored assuming each successive time (e.g. minute) being the real-timepoint. Each analysis can be abstracted into a thumbnail and stored in acollection of analyses thumbnails.

The execution of an analysis per sample rate (e.g. per minute) can put asignificant strain on resources. In-memory versions of channels andassociated time series are required to allow the rapid creation of theseobjects.

FIG. 23 is a block diagram of a hierarchical channel object inaccordance with an exemplary embodiment of the present invention. Thediagram is generally referred to by the reference number 2300. Asillustrated in FIG. 23, an exemplary embodiment of by present inventionmay be adapted to mitigate resource consumption by providing a processwherein a channel has been refactored into an abstract class (hereincalled “channel”) and two sub-classes (herein called “persistentchannel” and “in-memory channel” respectively). In FIG. 23, the channelobject is identified by the reference number 2302. The in-memory channelobject is identified by the reference number 2304 and the persistentchannel is identified by the reference number 2306. The persistentchannel 2306 may be database-aware and can represent a channel that hasbeen stored in the database as part of a case. The in-memory channel2304 may be a lightweight class with no ties to the database. In-memorychannels can be created from persistent channels (with a subset, orwindow of the time series).

In one exemplary embodiment of the present invention, the analysisbuilder classes have been refactored to be polymorphic (i.e. to workagainst either in memory or database-generated channels. In a similarway, the time series may be refactored to support persistent andin-memory versions of the class.

In one example, a real time analysis object is made up of threeelements:

-   -   1. A selected sample rate that indicates the granularity of the        analyses collection;    -   2. A snap analysis style—either window, window-plus-thumbnail or        past-omniscient; and    -   3. A collection of snapshot thumbnails abstracted from snap        analyses executed at the real-time points indicated by the        selected sample rate        This object can be stored along with a standard analysis to        provide a simulation of what could have been ascertained in real        time.

The user of the software can simulate moving through the time series,which may represent a night of sleep study data, and understand thereal-time conditions. This can be used for training purposes or for theanalysis, as discussed below, of alarming mechanisms.

FIG. 24 is a block diagram of a hierarchical time series object inaccordance with an exemplary embodiment of the present invention. Thediagram is generally referred to by the reference number 2400. Theexemplary time series object illustrated in FIG. 24 comprises atimeseries object 2402, a PersistedTimeSeries object 2404 and anInMemoryTimeSeries object 2406.

Once a real time analysis object such as the time series object 2400 isavailable, a new type of time series can be derived, displayed,manipulated, and transformed. A real time analysis object is acollection of data structures associated with contiguous data points.From this collection, any number of time series can be generated. Anyvalue or calculation can be specified against the snap analysisthumbnail to generate a series of contiguous values. This set ofcontiguous values represents data parallel in the same time set of theoriginal channel or set of channels from which they were derived. Inother words, any function that can reduce a snap analysis thumbnail intoa single value can create a new channel. This channel has exactly thesame general properties and characteristics as any other channel withinthe system. Transformations could be applied (e.g. smoothing). The datacan be displayed in a graphical format. For example, if the averagerecovery interval was considered critical in the real time analysis, thesystem could show a graphical representation of what the averagerecovery interval was at any sample real-time point in the time series.A significant increase in the recovery interval would be represented asan upward trend on the x-axis.

Examples for creating a separate channel can be accomplished byproviding the following:

-   -   1. A real time analysis object. This will provide the sample        rate and the collection of snap analysis thumbnails from which        the values will be obtained.    -   2. A function that will derive a single value when applied to a        snap analysis thumbnail. This can be the specification of a        value within the thumbnail or a calculation using the values        contained in the thumbnail. The time location can also be        used—e.g. an algorithm may follow a different path with        real-time points that are within the first 30 minutes of a case.    -   3. A range of the channel. This can be auto-generated as the        minimum and maximum of the values actually obtained from the        function or can be specified directly.

Since a real time analysis channel can have the same characteristics asany other channel, it can be subject to the same analysis as any otherchannel. Patterns of change can be identified using the same mechanismthat generated the set of original analyses. For example, the systemcould identify a trend in the recovery interval and create an event fromwhich can be derived all of the information associated with an event(e.g. slope and duration). Reciprocations, and even clusters could beidentified in the same way.

In one exemplary embodiment, the real time analysis channel provides aneffective mechanism for defining an alarm. An alarm could be based onthe following examples although many other mechanisms are possiblewithin the scope of this teaching:

-   -   1. The identification of a real time value falling into a        particular range. Any number of ranges could be used with        severities attached.    -   2. A function could be used to create a dynamic range. Threshold        values could be variable dependent on the location within the        time series (e.g. in the first 30 minutes) or be adjusted        according to values earlier in the real time analysis.    -   3. The alarm could be based on the properties of objects within        the analysis of the real time analysis channel (e.g. the        duration and/or slope of a trend of the average recovery ratio).    -   4. The alarm could examine relationships within the final snap        analysis thumbnail, which represents the most comprehensive        analysis given a specific real-time point. For example, the        alarm could look at whether the real-time point is in a cluster        and the characteristics of the last five reciprocations. These        relationships could be quantified and measured against a static        or dynamic range.    -   5. An alarm may look at a combination of characteristics of the        real-time channel and the final snap analysis thumbnail.    -   6. An alarm my look at a combination of a real time analysis        channel and the parallel native channels (e.g. the change the        average recovery channel along with the activity in the chest        wall channel).

The representation of a separate channel provides an effective mechanismfor reviewing results of an alarm. Experts can define an alarm andimmediately test its veracity. The system can highlight sections of thetime series (in either native channels or in the real time analysischannel) to indicate the results and severity of an alarm.Alternatively, a separate channel could display alarm results as a stepfunction. If thresholds are used to trigger and determine the severityof an alarm, the threshold values could be indicated on the appropriatechannels (either as a simple horizontal line or a curve if a function isused).

A set of representative cases could be manually marked by an expert withthe identification and severity of an alarm. The system could runthrough the set of cases to identify how closely the algorithmic alarmcorrelates with the manual review of an expert.

The following program listing is an example of an XML representation ofa channel analysis thumbnail: <ChannelAnalysisThumbnail> <CaseName>MonoTo Poly</CaseName> <CaseDescription>Patient exhibits a moderate SpO2delta. Progresses from monomorphic pattern to polymorphic withincomplete recovery cluster.</CaseDescription><CaseNumber>2</CaseNumber> <ChannelCategory>Oximetry</ChannelCategory><ChannelType>0</ChannelType><DurationInMilliseconds>33898000</DurationInMilliseconds><NumberOfClusters>18</NumberOfClusters> <ClusterThumbnails><ClusterThumbnail> <ClusterType>Symetrical ReciprocationCluster</ClusterType><CorrelatedToSleepStage>false</CorrelatedToSleepStage><PercentageInArtifact>0</PercentageInArtifact><PercentageInWake>0</PercentageInWake><PercentageInStage1>0</PercentageInStage1><PercentageInStage2>0</PercentageInStage2><PercentageInStage3>0</PercentageInStage3><PercentageInStage4>0</PercentageInStage4><PercentageInNonREM>0</PercentageInNonREM><PercentageInREM>0</PercentageInREM><Morphology>Monomorphic</Morphology><MeanStartEventDurationInMilliseconds>25400</MeanStartEventDurationInMilliseconds><MeanEndEventDurationInMilliseconds>12360</MeanEndEventDurationInMilliseconds> <MeanStartEventMagnitude>−8.523999</MeanStartEventMagnitude><MeanEndEventMagnitude>8.539998</MeanEndEventMagnitude><MeanStartEventSlope>−0.341138542</MeanStartEventSlope><MeanEndEventSlope>0.698189259</MeanEndEventSlope><MeanReciprocationMaxValue>96.49201</MeanReciprocationMaxValue><MeanReciprocationMinValue>87.564</MeanReciprocationMinValue><MeanReciprocationMagnitude>8.928</MeanReciprocationMagnitude><MeanReciprocationDurationRatio>2.10189247</MeanReciprocationDurationRatio><MeanReciprocationMagnitudeRatio>1.01252</MeanReciprocationMagnitudeRatio><MeanReciprocationSlopeRatio>0.5337045</MeanReciprocationSlopeRatio><MeanRecoveryDurationInMilliseconds>16917</MeanRecoveryDurationInMilliseconds ><MeanRecoveryRatio>0.762648046</MeanRecoveryRatio><NumberOfReciprocations>25</NumberOfReciprocations><ReciprocationThumbnails> <ReciprocationThumbnail><StartEventDurationInMilliseconds>9000</StartEventDurationInMilliseconds><StartEventSlope>−0.455555379</StartEventSlope><EndEventDurationInMilliseconds>9000</EndEventDurationInMilliseconds><EndEventSlope>0.366666168</EndEventSlope><Magnitude>4.09999847</Magnitude> <SlopeRatio>1.24242544</SlopeRatio><MagnitudeRatio>1.24242556</MagnitudeRatio><DurationRatio>1</DurationRatio><MajoritySleepStage>−1</MajoritySleepStage> </ReciprocationThumbnail> .. . </ReciprocationThumbnails> </ClusterThumbnail> . . .</ChannelAnalysisThumbnail>

In one exemplary embodiment of the present invention, a trigger is usedto enhance the detection of hypoventilation. A fall in SpO2 can commonlyoccur from hypoventilation or from a V/Q mismatch. A fall in SPO2 due tohypoventilation is often rapidly reversed by patient stimulation (forexample by an auditory pulse oximetry alarm which wakes the patient upor by the fall itself or the rise in CO2 that accompanies the fall)whereas a fall in SPO2 from a V/Q mismatch is not rapidly reversed bypatient stimulation. These represent various stimulus events, which canbe detected to identify a response to the stimulus. One of the problemswith the use of a SPO2 alarm is that fall in SPO2 may be reversed by thealarm induced by the fall or by the nurse who wakes the patient up uponresponding to the alarm. This preempts the fall but this preemption maybe early enough that the pattern of the SPO2 time series, which wouldhave been evident if not preempted, is not available for analysis sothat the nurse is not advised as to the nature of the fall. This mayprevent the detection of dangerous types of unstable hypoventilationsuch as a type II and Type III instability discussed previously sincethe nurse may be inclined to simply add oxygen, which may not be thebest choice of therapy. However, one exemplary embodiment of the presentinvention provides a processor which can record and/or receive anautomatic or manual indication of the occurrence of a stimulation eventand/or which can induce patient stimulation (such as an auditory alarmor a patient mounted vibration inducer). The processor may be programmedto identify the occurrence of a stimulation event (which may theexternally applied or may represent a physiologic stimulus, such as afall in oxygen saturation or a rise in CO2, from the patient under test)and to identify the occurrence of a pattern or value or range of valuesindicative of recovery, such as a recovery event subsequent to thestimulation event. The processor can be programmed to output anautomatic indication of the occurrence of at least one precipitousrecovery event within a short time interval (such as for example about2-15 seconds) after the onset of the stimulation event. In this way thenurse is notified that the fall in SPO2 was likely due to unstablehypoventilation. Another problem is that the conventional bedside alarmmay not be loud enough to awaken the patient so that preemption of aprofound and potentially fatal episode of hypoventilation may depend ontimely arrival of the nurse. To solve these problems upon detection ofan episode of profound fall in SPO2, a very forceful (crisis alarm)stimulation can for example be delivered to the patient by a bedsidemonitor or by an attached headphone, a collar for auditory or vibrationstimulation or another patient mounted stimulator. According to oneembodiment of the invention, a patient receiving parenteral narcoticsusing a patient-controlled analgesia pump has the conventional basicsalarm system for notifying the nurses as well as a second tier crisisalarm system and/or stimulator intended to stimulate and awaken thepatient in the event of a life threatening decline in SPO2. Regardlessof the cause of the stimulation, the detection of rapid “StimulationInduced Oxygenation Recovery” (“SIOR”) can provided as an automaticoutput by the processor. SIOR provides strong evidence that the cause ofthe fall in oxygen saturation is antecedent unstable hypoventilation andthat oxygen therapy may not be the best choice.

One exemplary method for using an oximeter for detecting response of apatient to a stimulus can comprise generating at least one time seriesof at least one physiologic parameter derived from the oximeter,automatically detecting the stimulus, detecting at least one responsesubsequent to the stimulus, the response comprising at least one of apattern and a value and outputting an indication of the response to thestimulus. The response can be a value indicative of the peak value, avalue indicative of a magnitude value, such as a magnitude of a rise inoxygen saturation. The response value is a value indicative of a peakvalue of oxygen saturation and/or magnitude of rise in an oxygensaturation value (as for example defined by the nadir to peakdifference) that follows the stimulus (such as the alarm) or whichfollows a manually applied stimulus by the nurse (in this situation, theprocessor can be programmed to receive an input, which may be a manualinput, indicating the occurrence of a stimulus). The response value canbe a value indicative of a change in a component of the plethesmographicpulse such as the rise in the plethesmographic pulse amplitude thatfollows the stimulus.

In another exemplary embodiment of the present invention, the monitorincludes an alarm-response tracking system. One of the problems withpresent hospital environments is that personnel may respond slowly toalarms and the response time in a given hospital or on a given ward isgenerally unknown to the hospital administration. Yet the“Alarm-to-Bedside-Response-Time” (ABRT) may be an important factor inpatient well-being. An exemplary embodiment of the present inventionincludes a user interface for inputting an indication that a response tothe alarm has occurred. The processor may record and output at least tworesponses; The physiologic response of the patient subsequent to thealarm (including the presence or absence of SIOR and the delay betweenthe response, if any), and the alarm and the nurse response subsequentto the alarm (including the delay between the response, if any and thealarm). Using these data sets, hospitals can identify delays by hospitalward, by shift, an by many other factors. One issue can be that thenurse, arriving at the bedside needs to examine the patient as quicklyas possible and perhaps should not be delayed by having to input abedside response into the monitor. However, it can be hospital policythat, if the patient is in distress or appears in crisis, that theresponse time need not be recorded. The processor can include an inputthat indicates that the nurse forgot to input the response or was toobusy to do so. Since there are many non-distress alarms, thesevariations in recording should not affect the validity of the quality ofthe ABRT datasets.

In one exemplary embodiment of the present invention, a severity indexis determined using a combination of a plurality of waveform featuresthat are indicative of severity of instability. Examples of such indiciainclude a combination of one or more of the recovery threshold, therecovery response, and the duration of objects (such as eventsreciprocations or cluster) or other features. In an example, thesefeatures can include at least one value indicative of at least one nadirrelationship, at least one value indicative of at least one peakrelationship, and at least one value indicative of duration such as thearea above at least a portion of a cluster. The nadir or peakrelationship can be the absolute nadir or peak value, a differencebetween the nadir and/or peak value and another value or measure, arelationship between consecutive peaks and or nadirs, a relationshipbetween the fall and rise amplitudes, or a relationship between rangesof a plurality of peaks and nadirs. These values can be weighted. Forexample, the calculation can be weighted such that a poor arousalresponse greatly affects the index. One example of a weightedinstability index calculation is (98-Nadir)+4(94-Peak)+(area given insaturation seconds of desaturation below 98 over an interval of Xminutes/60Y). For example, X can be about 3-15 minutes although othervalues may be used and Y any of a range of values including equal to X.The index can be provided, for example, on a numerical scale having in arange from 1 to 100 with the final calculation adjusted to that rangeproportionally or by considering all values in excess of 100 to be equalto 100. This index can be calculated, for example, for each a movingwindow of time. A few examples of moving window of time include:

-   -   A series of contiguous sections of the time series with a fixed        time interval. As an example, a series of 1-minute sections that        are adjacent to each other but not necessarily intersecting.    -   A series of contiguous or near contiguous sections of the time        series with a variable time interval. As an example, a time        interval that can change depending on characteristics within the        time series, a response to a user gesture, according to a        mathematical function, and/or randomly to name a few.    -   A series of non-contiguous sections with a fixed time interval        with a fixed or variable separation in between.    -   A series of non-contiguous sections with a variable time        interval. As an example, a time interval that can change        depending on characteristics within the time series, a response        to a user gesture, according to a mathematical function, and/or        randomly to name a few.    -   A series of overlapping windows. In an example, a sample point        can be chosen for each X seconds (e.g. 10) and the previous Y        minutes (e.g., 5) can be analyzed. The beginning of the window        can be truncated such that for any sample point less than 5        minutes the window will be less than 5 minutes depending on the        available data between the sample point and the beginning of the        time series.    -   Non-discrete windows. For example, windows that can        conditionally include points outside of their boundary (e.g. the        minimum point of a rise event that falls within the window which        could be outside of the window boundary).        Since a low SPO2 is indicative of instability risk, which is not        only the function of the oxygen deficit but also as a function        of factors that allowed the SPO2 to fall to a low level and of        factors (such as the commonly increased CO2), which accompanies        a given low SPO2. The SPO2 value can be weighted for severity of        the absolute value itself as, for example, the (100-SPO2 value)        squared and then divided by 10. The value of a given measure or        calculation may be weighted for the presence of a pattern such        as a cluster pattern (as for example a threshold pattern of        clusters) as detected by any method. Examples for the detection        and/or quantification of a clutter pattern include, the        detection of a threshold difference between a plurality maximum        and minimum values detected within an interval as defined for        example within a series of contiguous, but substantially        non-intersecting, sections of the time series (e.g., minute        sections), the detection of a temporal, spatial, or frequency        pattern (as by a transform) indicative of threshold clustering,        or the detection of a specific object such as clusters or a        pattern of objects such as a plurality of reciprocations, or        events to name a few. Maximum value may be defined as the        maximum value or a conditionally identified maximum within a set        of maximum values. For example, a series of minimum values could        be derived from a moving window within a specified section of        time and a specific maximum value could be conditionally        determined according to thresholds, or through other        characterizations of the waveform (for example if a particular        section of the waveform is considered artifact, specific values        may be discarded). Minimum value may be defined as a minimum        value or a conditionally identified minimum value within a set        of minimum values. An example of the weighting applied if        clustering is detected follows. Using an oximeter, a time series        of SPO2 values are determined, the processor is programmed to        detect clusters and nadirs within clusters, the calculation 2        (98-Nadir) can then be applied if a cluster is detected and the        nadir is part of a fall event thereby indicating that the nadir        value is a component of an unstable SPO2 pattern. These choices        for severity indexing and various options for weighting can be        provided in a menu, which is especially useful for the        researcher. For example, the calculation 2 (98-Nadir) may be        used if a cluster is detected prior to the determination of the        Nadir and the nadir is part of a fall event (which indicates        that the nadir value is a component of a unstable SPO2 pattern).        These choices for severity indexing and weighting can be        provided in a menu, which is especially useful for the        researcher.

Alternatively, in another example, the window can vary with the objectssuch that a prolonged continuous cluster of a given cluster type canprolong the window as with the denominator (below the area calculation)being adjusted for the time and the nadir can be the mean nadir or thelowest nadir and/or the peak can be the mean peak or the lowest peak. Inone example, a plurality of parallel time series of severity can begenerated, each containing at least one component severity. For example,a time series indicative of a plurality of at least one nadirrelationship can be generated parallel to a time series indicative of aplurality of at least one peak relationship. The patterns of each ofthese times series may be then be analyzed to define events,reciprocations, and clusters. A severity index can then be similarlycalculated using the pattern relationships of one or more of theseverity time series. The pattern relationships of one or more of theindices of severity can be quantified using pattern recognition andanalysis software for example of the types disclosed previously. This isone example of providing a way to quantify the patterns, absolutevalues, and relationships of the components of the aggregate severityindex to provide additional diagnostic utility.

The above system method for severity indexing can be applied to a broadrange of physiologic signals. For example, a plethesmographic pulse timeseries (such as a time series of the amplitude of the plethesmographicpulse) can be similarly processed. In one exemplary embodiment, the timeseries of the SPO2 and the time series of the amplitude of theplethesmographic pulse are analyzed together in parallel. If oxygen isapplied, the severity of the instability of the plethesmographic pulseis used as a marker of severity.

In one exemplary embodiment of the present invention, an interpretiveoximeter is capable of at least one of a textual indication of thepattern detected and a severity index value (such as the severity indexvalue associated with that pattern) which may be automaticallydisplayed. For example, upon the detection of an incomplete recoverycluster, the processor may be programmed to output “UnstableHypoventilation with Incomplete Recovery” and also display a severityindex of 78, which displays for the caregiver the severity of theunstable hypoventilation pattern. In one exemplary embodiment, alldetected patterns, (including the failure patterns) or a discretionaryrange of detected patterns, are stored for retrieval by the nurse as byproviding a “review detected SPO2 or Pleth patterns” icon on theoximeter or central monitor.

In one exemplary embodiment, the severity indexing and/or alerts areadjusted for the presence of oxygen therapy (as by nasal cannula). Thepresence of oxygen therapy is inputted either manually of automatically.For example, the processor can include an input for manual designationof oxygen therapy on the display or can be programmed to receive theinformation relevant to the presence of oxygen from the patient'selectronic chart, or the monitor may have a sensor which connects to thebedside oxygen source which automatically senses the flow of oxygen. Inone example, oxygen delivery indicator, such as a flow-sensing monitorcan be mounted on the oxygen gas port. The flow or pressure sensingmonitor can have a connector for connecting to the oxygen gas port atone end, and the conventional gas delivery port at another end. This canbe attached, as by threading onto the oxygen gas port when the oximeteris in use (or can be permanently connected to the rooms bedside thelocation occupied by the oximeter). The flow or pressure-sensing monitorincludes a sensor, which identifies the presence of flow or pressure inthe sensor, which is mounted at the port distal the oxygen flow valve.The presence of pressure sends a signal (such as an analog signal ordigital signal) to the oximeter indicating the presence of oxygen flow(and the flow rate if desired). The processor is programmed to adjustthe severity index and alarms for the presence of oxygen flow orpressure. This adjustment can be, for example, indicating all clustersas severe in the presence of oxygen or providing a weight for thepresence of oxygen therapy (for example multiplying the severity indexby about 3-4 or adding a value such as 50% of the maximum index to thecalculated index. Alternatively the oxygen valve at the oxygen sourcecan be an electronic valve, which provides an output such as a digitaltransmitted output of the flow rate being delivered or a flow sensor canbe mounted on the flow tubing extending to the patient or otherwisealong the flow path between the oxygen source and the patient internalairway. When a real-time oxygen delivery indicator is provided it can becombined with a nasal pressure and/or CO2 monitor so that both oxygendelivery, nasal catheter and/or mask position, and the pattern ofpatient response can be monitored.

As discussed previously, specific spatial, temporal, and frequencypatterns vary as a function of specific pathophysiologic mechanisms,each of which produce a specific pattern type. Also, within eachpathophysiologic mechanism, specific types of biologic failure modes canoccur which produce specific patterns of deviation from the pattern typeoccurring without the failure. For this reason, an exemplary embodimentof the present invention generates an output, which renders acontemporaneous output of at least one of the detected adverse patterntype (if any), the detected failure mode and failure mode pattern type(if any), and sequential values or indicators indicative the magnitude,pattern, and/or trend of instability, such as a time series ofinstability integers (as, for example, derived form an aggregateanalysis of the instability of the pattern). For the purpose ofillustration, the factors derived of the patterns, which define clinicalinstability, can be divided into two main component classes: Class 1 cancontain pattern components, which potentially induce or increase theprobability of substantial adverse of an organ or organ system. Suchadverse condition or injury include cardiac arrhythmias, cardiacischemia, brain ischemia, enhanced thrombogenesis, and/or patientconfusion to name a few. Examples of pattern subcomponents of Class 1include high amplitude of the oxygen deficit as defined for example by alow SPO2 nadir, rapid progression of oxygen as defined for example by asteep slope SPO2 fall, high duration of the oxygen deficit as defined bya prolonged SPO2 fall, a short recovery interval, a high area above aportion of the SPO2 curve, the presence of cycling (which can result inautonomic stimulation and increased regional oxygen consumption) asdetected by a wide range of measures, for example, frequency transforms,peak to trough detection, template comparison, to name a few. Class 2can contain pattern sentinel components and subcomponents, whichindicate an increased probability of progression of general instabilityas, for example, progression to stupor, coma, and/or respiratory arrest.A few examples of pattern subcomponents of Class 2 include variabilityof the arousal threshold (arousal threshold instability), increasedarousal threshold (arousal threshold failure), and incompletephysiologic response to the perturbation (recovery failure).

The pattern components of the basic patterns of the pathophysiologicmechanisms and/or the pattern components indicative of a specificfailure mode can be derived from a plurality of parameters and/or aplurality of processing methods. For example, an index combining aspatial pattern component of a time series of SPO2 may be combined withfrequency component of the pleth to produce an aggregate componentindicative of a magnitude of adverse perturbation. In another example,sequential measures of the maximum to minimum difference of the plethamplitude (or pleth derived pulse rate) combined with sequentialmeasures of the maximum to minimum difference of the SPO2 value, can beused to aggregate component indicative of the magnitude of perturbation.These pattern components and aggregate components may be detected andquantified by a wide range of techniques. Examples include, time seriesobjectification, frequency transforms, template comparisons, adaptivemethods, and/or the application of a moving window (for example a 20-180second window) as with a set of rules for detecting minimum and maximumvalues and the spatial, temporal and/or frequency relationships such asthe differences or distributions of or between these patterns to name afew.

One exemplary embodiment can produce a continuous instability index timeseries based on one or more, or at least, the following weightedfactors; an indication of a minimum value or nadir relationship as, forexample, a selected value minus the nadir value, an indication of amaximum value or peak relationship as for example a selected value minusthe peak value, an indication of an area in relationship to the curvesuch as the product of the saturation seconds above the curve (and belowa reference value if preferred which value can be varied with thedetection of the presence of cycling). With any of these calculations,absolute value can be weighted for its difference from a normal or otherreference value. For example, a greater weight (as a function of thevalue itself) can be applied to a SPO2 fall of 5 between 85 and 80 thena fall of 5 between 80 and 75. This is a useful weighting approachbecause, despite the presence of identical magnitudes of fall, the firstfall of 5 is indicative of less inherent instability than the secondfall of 5. Also, a greater increased severity weight (beyond thedifference of 10) can be included in any subsequent calculation suchthat additional weight may be given to a data point having absolute SPO2value of 70 then to a SPO2 data point having a value of 80 which weightis greater than the weight ascribable to their relative positions below90. This type of weighting is particularly useful when the parametercomprises an absolute reference values (values in which the absoluterange of normality is known) such as the CO2 or SPO2, the absolute valuecan be weighted for its difference from a normal or other referencevalue. An example of how such weighting can be derived discussed earlierfor the SPO2 parameter. This type of value adjusted weighting can byused to derive an enhanced saturation seconds calculation where a givenlow SPO2 provides a greater weight than a higher value as a function ofits absolute value, its difference from a reference value or by anothermethod of weighting based on the relationship of the value to anothervalue, a pattern, or a reference value. In this way, an instabilityindex can be derived that is weighted as a direct function of theposition of any given measured SPO2 or CO2 value in relation to areference value or to the detection of a pattern such as a specificpattern of cycling. For example, an SPO2 value recognized at the nadiralong a type IV pattern may be given less weight then an identical SPO2value recognized at the peak along a type IV pattern. In anotherexample, an SPO2 value recognized at the nadir along a type IV patternmay be given less weight then an identical SPO2 value recognized at thenadir along the more fundamentally unstable type III pattern. In each ofthese exemplary embodiments, the processor can be programmed to producean automatic textual output of the aforementioned detected pattern typeor types, an automatic textual output of any detected failure mode, suchas arousal threshold failure or recovery failure, and a time series ofthe calculated aggregate or component severity index which relates tothe severity of the pattern and the severity of the failure. In thisway, the processor is programmed to output for the nurse in real time atthe bedside and or central station, a display of the timed parameter, anautomatic text indication of the pathophysiologic pattern types withreviewable archived text indications and the associated pattern, a textindication of the failure modes with reviewable archived textindications of failures and the associated failure pattern, and a timednumerical or other scale indication of the aggregate instability whichis archived, reviewable at a range of scales, and analyzable forpatterns of aggregate instability. The time series of aggregateinstability can be incorporated into the cylindrical data matrix orotherwise used to compare the pattern or absolute values of aggregateinstability with other parameter patterns, exogenous actions such asdrug infusion, patient location (as by a time series output of a GPSrecorder), or expense to name a few.

One exemplary embodiment comprises a monitor, a processor programmed todetect a maximum value, and to detect a maximum value below an expectedor reference maximum value. The maximum value can be indicative of arecovery the maximum value below an expected or reference maximum valuecan be indicative of an incomplete recovery. The monitor can be anoximeter such as a pulse oximeter and the incomplete recovery cancomprise an incomplete reciprocation as defined by a magnitude, slope,shape, timing or other relationship between a fall and a rise. Inanother example the incomplete recovery can comprise an incompletereciprocation as defined by the absolute value of the peak or by arelationship between an absolute value of the peak or maximum value ofthe recovery and a reference value (such as a normal value). Forexample, the relationship can be a spatial relationship, a calculateddifference, or a distance to name a few. The presence of a recovery canbe detected by a wide range of methods or combinations of method or canbe inferred (as, for example, by detecting a maximum value within aspecified prolonged window or windows of time after a point whereinclustering is initially detected. In another example, the relativeequivalent of a maximum value of at least one recovery within the windowcan be assumed if the detected minimum value within or adjacent thewindow is different, as by a pre-selected amount than a subsequentdetected maximum value and wherein the detected maximum value is not thelast value in the window.

One exemplary embodiment of the present invention comprises a monitor, aprocessor programmed to detect a minimum value, and to detect a minimumvalue below an expected or reference minimum value. The minimum valuecan be indicative of the nadir of a fall and the minimum value below anexpected or reference minimum value can be indicative of clinicallysignificant fall. One exemplary embodiment comprises a monitor, aprocessor programmed to detect a nadir and/or to detect a failing nadir(as with arousal threshold failure) or an unstable pattern of nadirs.The monitor can be an oximeter such as a pulse oximeter and presence ofunstable nadirs can comprise nadir, which varies in relation to othernadirs as for example defined by a threshold pattern, slope, timing orshape of a plurality of nadirs. The nadirs can also be defined as amagnitude, slope, shape, timing or other relationship between a fall anda rise. The nadir can be defined, for example, by a relationship betweenan absolute value of the nadir, or minimum value of the fall, and areference value (such as a normal value). For example, the relationshipcan be a spatial relationship, a calculated difference, or a distance toname a few. The presence of a fall and of a nadir can be detected by awide range of methods or combinations of method or can be inferred (as,for example, by the detecting minimum value within a specified window oftime, which is then assumed to be the relative equivalent of a minimumvalue of at least one fall within the window). In an example, thisinference can be made if the detected minimum value within or adjacentthe window is different, as by a pre-selected amount than the detectedmaximum value. The above approach is useful for parameters, which fallwith increasing perturbation, such as SPO2; the CO2 often rises inassociation with the SPO2 falls induced by increasing levels ofhypoventilation. For this reason, the peak of the CO2 in the abovediscussed processing can be used as indicative of the magnitude of theadverse event and the nadir to the recovery from the adverse event.

In an example using an oximeter, a method for monitoring a patientcomprises, placing a probe of an oximeter adjacent a body part,outputting a time series of SPO2, detecting at least one of a pluralityof minimum and maximum values of SPO2 along the time series, using atleast one of the plurality minimum and maximum values, determining anindex of the severity of a pathophysiologic process such as unstablehypoventilation. The method can further comprise programmaticallydefining a moving window for the detection of the minimum and maximumvalues. The method can be similarly applied to CO2 monitors othermonitors or to combined SPO2, CO2 and or other monitors.

In one embodiment, the processor is programmed to calculate a pluralityof sequential and frequently updated values indicative of the global ornear global instability of the patient which can be a time series of thepatient's Global Instability Index (GII). The global instability indexcan be derived from the processing of the spatial, temporal, andfrequency patterns and relationships of a plurality of numerical,spatial, temporal, and/or frequency derivatives of parameters such asoxygen saturation, exhaled carbon dioxide, respiratory rate, pulse,temperature, and blood pressure, to name a few. The global instabilityindex can be derived of the patterns of at least one parameter comparedwith the pattern of at least another parameter and/or the absolute valueof at least another parameter. The known presence of a potentiallyvolatile disease or disorder or of a particular vulnerability can beinduced to enhance the value of weighting. For example, if a patient isknown to have coronary artery disease and/or a recent myocardialinfarction the weight applied to the presence of clustering and/or tothe nadir value of the SPO2 can be adjusted so that the patient withparticular known vulnerability is identified as potentially moreunstable by manifesting a higher or otherwise more severe real timeindex in the presence of clustering then a patient without thisvulnerability. The nadir component can, for example, be doubled and thenapplied to derive SPO2 instability index which is then combined withderivatives of other parameters to output the global instability index.Alternatively, the SPO2 instability index could be weighted for thepresence of coronary artery disease after it is calculated as a combinedindex or the global instability index could be weighted for the presenceof coronary artery disease after it is calculated as a combined index.Additional volatile diseases include, for example, congestive heartfailure, Type I diabetes, severe hypertension, sepsis, pulmonaryembolism, pulmonary hypertension to name a few.

In one exemplary embodiment, a patient monitor is provided thatcomprises an instability detection and tracking monitor. The monitorpreferably includes a large screen (which can be a pop-up screen) sothat multiple time series and multiple instability indicators and/orinstability time series can be displayed. For example, for the bedsideversion, a configuration with a large display as well as a largedisplay-to-size ratio, similar to a tablet PC is suitable. A displaythat allows various time series to be scrolled up into view, is alsoacceptable to reduce size. The processor can be programmed toautomatically move the most unstable time series into view upon, as onthe occurrence of a threshold instability value or pattern. Theinstability monitor preferably includes a pulse oximeter and at leastone ventilation monitor (such as a flow monitor, pressure monitor,impedance or other chest movement monitor, spirometer), and/or a CO2monitor, and/or and at least one cardiovascular monitor (which can bethe pleth portion of the pulse oximeter). The processor of theinstability detection and tracking monitor may be programmed to definethe SPO2 instability components and their subcomponents, thecardiovascular instability components and their subcomponents, and theventilation instability components and their subcomponents. Theprocessor can be programmed to determine sequential instability indexcalculations for each parameter and then to combine them to rendersequential, unifying aggregate instability index calculations, which canbe adjusted as discussed above for the presence of a disease, condition,other parameter, and/or pattern which is potentially fundamentaldestabilizing. In an example, each parameter specific instability indexcan render a value on a numerical scale between 1 and 100 as for exampledescribed for SPO2. Such a numerical may be finite or unbounded. Theindex of each of the parameters can be additive to render a globalInstability index also with a value range of 1 to 100 (where indexvalues above 100 are given as 100). In this way, a profound instabilityas potentially of any one parameter will trigger the maximum instabilityindex whereas even a moderate instability, which is sufficient to causesubstantial perturbation of all three, may trigger the maximum value.Also, combined patterns, such as the presence of divergence of SPO2 andrespiratory rate may generate a minimum value to be added to the globalinstability index (such as 50), which is sufficient to reliably triggerthe recommendation of a protocol, by the processor as discussed below.

In an exemplary embodiment, a threshold breach, trend or pattern of aparameter specific real-time or otherwise frequently updated instabilityindex is used to trigger a protocol, which can be an assessmentprotocol, which for example calls for (or automatically triggers)additional testing, monitoring or treatment. The protocol can include arequisite entry based on the assessment of a health care worker such,for example, a review and confirmation of the SPO2 pattern before an theautomatic positive airway pressure treatment and monitoring protocol istriggered. Using this embodiment, the processor can be programmed toidentify an instability pattern type along a parameter, identify afailure pattern indicative of a specific failure mode (if any),determine a frequently updated instability index (which can be a timeseries of the instability index), identify a threshold level or patternalong the instability index, of the instability index of instability ofa single parameter, trigger a protocol based at least one of theparameter, the detected pattern type (with or without health care workerconfirmation of the pattern type), the failure mode, and the thresholdlevel which triggered the protocol. The processor can be furtherprogrammed to; identify an instability pattern type along a plurality ofparameters, identify a failure pattern along each of the parameters ofat least one specific failure mode (if any), determine a frequentlyupdated instability index for each parameter (which can be a time seriesof the instability index), add or otherwise combine the instabilityindex to produce a global instability index, identify a threshold levelor pattern along the global instability index or at least one of theinstability index of the parameter, the detected pattern type (with orwithout health care worker confirmation of the pattern type), thefailure mode, and the threshold level which triggered the protocolwherein the specific triggered protocol type triggered by theprocessor's determination of the detected pattern type (with or withouthealth care worker confirmation of the pattern type). A typical protocoltriggered by the processor, and outputted by the processor if desired,can include for example; review the patterns for confirmation ofunstable hypoventilation “physician accepts pattern?”, If yes, physicianshould also to review narcotic and sedative prescription and adjust asnecessary, also if yes, call AutoPAP administration team (the processorcan be programmed to automatically by the processor, fit mask andadminister training, apply low acclimation pressures while patient isawake, review history, any patient history of CPAP use, if yes, applyminimum pressure as previously prescribed and maximum 5 cm higher,otherwise set min and max CPAP values at 5 and 12 respectively), connectautoCPAP output to the input port of the bedside instability trackingmonitor (if wireless connection is not available), have patient notifynurse when ready to fall asleep, when patient is ready to fall asleep,apply mask and turn on the autoCPAP device with the 3 minute ramp, usingthe instability monitor connected with the autoCPAP device monitor thepatterns. Call the AutoCPAP administration team if the SPO2 instabilityindex is not reduced below 20 and global instability index of less than30 or if the patterns are otherwise eliminated (this can be automatic asfor example a notification at the respiratory therapist office such as“incomplete instability mitigation at bed 6 A, 8 north Tower” or atanother station at a centralized control location. The protocol can alsoinclude secondary protocols based on the failure of correction of theinstability index or the new development of a rising or otherwise highinstability index despite the initiation of treatment.

Those skilled in the art will recognize that various changes andmodifications can be made without departing from the invention. Manydifferent pattern analysis methods, software tools, and mathematicalcalculations can be employed within the scope of the invention. Inparticular, it should be noted that the application of programmingmethods techniques such as, for example, adaptive programming, fuzzylogic, intentional programming, genetic algorithms and statisticalprocessing are included in this teaching. While the invention has beendescribed in connection with what is presently considered to be the mostpractical and preferred embodiments, it is to be understood that theinvention is not to be limited to the disclosed embodiments, but on thecontrary, is intended to cover various modifications and equivalentarrangements included within the spirit and scope of the appendedclaims.

1. A method of analyzing data, comprising: receiving data correspondingto at least one time series; and computing a plurality of sequentialinstability index values of the data.
 2. The method recited in claim 1,converting the plurality of sequential instability index values into aninstability index time series.
 3. The method recited in claim 2,comprising analyzing the instability index time series to detect atleast one of a pattern and a threshold.
 4. The method recited in claim1, comprising producing an output if at least one of the plurality ofsequential instability index values exceeds a threshold.
 5. The methodrecited in claim 1, comprising expressing at least one of the pluralityof sequential instability index values according to a numerical scale.6. The method recited in claim 5, wherein the numerical scale comprisesa finite range.
 7. The method recited in claim 1, comprising convertingat least one of the plurality of sequential instability index values tocorrespond to a numerical scale.
 8. The method recited in claim 7,wherein the numerical scale comprises a finite range.
 9. The methodrecited in claim 1, wherein the at least one time series includes dataindicative of an SPO2 level of a person.
 10. The method recited in claim1, wherein the at least one time series includes data indicative of aCO2 level of a person.
 11. The method recited in claim 1, wherein the atleast one time series includes data derived from a plethesmographicpulse.
 12. The method recited in claim 1, wherein the at least one timeseries includes data indicative of a respiration level of a person. 13.The method recited in claim 1, wherein at least one of the plurality ofsequential instability index values is characterized at least in part bya peak measure of the at least one time series.
 14. The method recitedin claim 1, wherein the peak measure comprises at least one of area,duration, magnitude, value, slope, spatial pattern, temporal pattern,frequency pattern, and shape.
 15. The method recited in claim 1, whereinat least one of the plurality of sequential instability index values ischaracterized at least in part by a nadir measure of the at least onetime series.
 16. The method recited in claim 1, wherein at least one ofthe plurality of sequential instability index values is characterized atleast in part by a clustering measure of the at least one time series.17. The method recited in claim 1, wherein at least one of the pluralityof sequential instability index values is characterized at least in partby a perturbation measure of the at least one time series.
 18. Themethod recited in claim 1, wherein at least one of the plurality ofsequential instability index values is characterized at least in part bya recovery measure of the at least one time series.
 19. A system,comprising: a source of data indicative of at least one time series ofdata; and a processor that is adapted to compute at least one of aplurality of sequential instability index values of the data.
 20. Thesystem recited in claim 19, wherein the processor is adapted to convertthe plurality of sequential instability index values into an instabilityindex time series.
 21. The system recited in claim 20, wherein theprocessor is adapted to analyze the instability index time series todetect at least one of a pattern and a threshold.
 22. The system recitedin claim 19, comprising an output device that is adapted to produce anoutput if at least one of the plurality of sequential instability indexvalues exceeds a threshold.
 23. The system recited in claim 19, whereinthe processor is adapted to express at least one of the plurality ofsequential instability index values according to a numerical scale. 24.The system recited in claim 23, wherein the numerical scale comprises afinite range.
 25. The system recited in claim 19, wherein the processoris adapted to convert at least one of the plurality of sequentialinstability index values to correspond to a numerical scale.
 26. Thesystem recited in claim 25, wherein the numerical scale comprises afinite range.
 27. The system recited in claim 19, wherein the at leastone time series includes data indicative of an SPO2 level of a person.28. The system recited in claim 19, wherein the at least one time seriesincludes data indicative of a CO2 level of a person.
 29. The systemrecited in claim 19, wherein the at least one time series includes dataderived from a plethesmographic pulse.
 30. The system recited in claim19, wherein the at least one time series includes data indicative of arespiration level of a person.
 31. The system recited in claim 19,wherein at least one of the plurality of sequential instability indexvalues is characterized at least in part by a peak measure of the atleast one time series.
 32. The system recited in claim 31, wherein thepeak measure comprises at least one of area, duration, magnitude, value,slope, spatial pattern, temporal pattern, frequency pattern, and shape.33. The system recited in claim 19, wherein at least one of theplurality of sequential instability index values is characterized atleast in part by a nadir measure of the at least one time series. 34.The system recited in claim 19, wherein at least one of the plurality ofsequential instability index values is characterized at least in part bya clustering measure of the at least one time series.
 35. The systemrecited in claim 19, wherein at least one of the plurality of sequentialinstability index values is characterized at least in part by aperturbation measure of the at least one time series.
 36. The systemrecited in claim 19, wherein at least one of the plurality of sequentialinstability index values is characterized at least in part by a recoverymeasure of the at least one time series.
 37. A pulse oximeter,comprising: a probe that is adapted to be attached to a body part of apatient to create a signal indicative of an oxygen saturation of bloodof the patient; and a processor that is adapted to receive the signalproduced by the probe, to calculate an SPO2 time series based on thesignal, and to compute a plurality of sequential instability indexvalues of the SPO2 time series.
 38. The pulse oximeter recited in claim37, wherein the processor is adapted to convert the plurality ofsequential instability index values into an instability index timeseries.
 39. The system recited in claim 38, wherein the processor isadapted to analyze the instability index time series to detect at leastone of a pattern and a threshold.
 40. The pulse oximeter recited inclaim 37, comprising an output device that is adapted to produce anoutput indicative of at least one of the plurality of sequentialinstability index values.
 41. The pulse oximeter recited in claim 37,wherein the processor is adapted to express at least one of theplurality of sequential instability index values according to anumerical scale.
 42. The pulse oximeter recited in claim 41, wherein thenumerical scale comprises a finite range.
 43. The pulse oximeter recitedin claim 37, wherein the processor is adapted to convert at least one ofthe plurality of sequential instability index values to correspond to anumerical scale.
 44. The pulse oximeter recited in claim 43, wherein thenumerical scale comprises a finite range.
 45. The pulse oximeter recitedin claim 40, wherein the output device is adapted to update the outputat a periodic interval.
 46. The pulse oximeter recited in claim 40,wherein the output device is adapted to update the output at a thresholdchange point along the SPO2 time series.
 47. The pulse oximeter recitedin claim 40, wherein the output device is adapted to update the outputat a threshold change region along the SPO2 time series.
 48. The pulseoximeter recited in claim 37, comprising an output device that isadapted to produce an output if at least one of the plurality ofsequential instability index values exceeds a threshold.
 49. The pulseoximeter recited in claim 37, wherein the signal is derived from aplethesmographic pulse.
 50. The pulse oximeter recited in claim 37,wherein at least one of the plurality of sequential instability indexvalues is characterized at least in part by a peak measure of the SPO2time series.
 51. The pulse oximeter recited in claim 37, wherein atleast one of the plurality of sequential instability index values ischaracterized at least in part by a nadir measure of the SPO2 timeseries.
 52. The pulse oximeter recited in claim 37, wherein at least oneof the plurality of sequential instability index values is characterizedat least in part by a clustering measure of the SPO2 time series. 53.The pulse oximeter recited in claim 37, wherein at least one of theplurality of sequential instability index values is characterized atleast in part by a perturbation measure of the SPO2 time series.
 54. Thepulse oximeter recited in claim 37, wherein at least one of theplurality of sequential instability index values is characterized atleast in part by a recovery measure of the SPO2 time series.
 55. Asystem for analyzing data, comprising: means for receiving datacorresponding to at least one time series; and means for computing aplurality of sequential instability index values of the data.
 56. Atangible machine-readable medium, comprising: code adapted to accessdata corresponding to at least one time series; and code adapted tocompute a plurality of sequential instability index values of the data.57. A method of analyzing data, comprising: receiving data correspondingto at least one time series; and searching the data to identify anincomplete recovery.
 58. The method recited in claim 57, comprisingsearching the data to identify a plurality of sequential incompleterecoveries.
 59. The method recited in claim 57, comprising producing anoutput indicative of the incomplete recovery.
 60. The method recited inclaim 59, comprising periodically updating the output.
 61. The methodrecited in claim 59, comprising updating the output if a thresholdchange point occurs along the at least one time series.
 62. The methodrecited in claim 59, comprising updating the output if a thresholdchange region occurs along the at least one time series.
 63. The methodrecited in claim 57, comprising producing an output if the incompleterecovery exceeds a threshold.
 64. The method recited in claim 57,wherein the at least one time series includes data indicative of an SPO2level of a person.
 65. The method recited in claim 57, wherein the atleast one time series includes data indicative of a CO2 level of aperson.
 66. The method recited in claim 57, wherein the at least onetime series includes data derived from a plethesmographic pulse.
 67. Themethod recited in claim 57, wherein the at least one time seriesincludes data indicative of a respiration level of a person.
 68. Themethod recited in claim 57, wherein the incomplete recovery ischaracterized at least in part by a peak measure of the at least onetime series.
 69. The method recited in claim 68, wherein the peakmeasure comprises at least one of area, duration, magnitude, value,slope, spatial pattern, temporal pattern, frequency pattern, and shape.70. The method recited in claim 57, wherein the incomplete recovery ischaracterized at least in part by a nadir measure of the at least onetime series.
 71. The method recited in claim 57, wherein the incompleterecovery is characterized at least in part by a clustering measure ofthe at least one time series.
 72. The method recited in claim 57,wherein the incomplete recovery is characterized at least in part by aperturbation measure of the at least one time series.
 73. The methodrecited in claim 57, wherein the incomplete recovery is characterized atleast in part by a recovery measure of the at least one time series. 74.A system, comprising: a source of data indicative of at least one timeseries of data; and a processor that is adapted to search for anincomplete recovery represented by the data.
 75. The system recited inclaim 74, comprising an output device that is adapted to produce anoutput if the incomplete recovery exceeds a threshold.
 76. The systemrecited in claim 74, wherein the at least one time series includes dataindicative of an SPO2 level of a person.
 77. The system recited in claim74, wherein the at least one time series includes data indicative of aCO2 level of a person.
 78. The system recited in claim 74, wherein theat least one time series includes data derived from a plethesmographicpulse.
 79. The system recited in claim 74, wherein the at least one timeseries includes data indicative of a respiration level of a person. 80.The system recited in claim 74, wherein the incomplete recovery ischaracterized at least in part by a peak measure of the at least onetime series.
 81. The system recited in claim 80, wherein the peakmeasure comprises at least one of area, duration, magnitude, value,slope, spatial pattern, temporal pattern, frequency pattern, and shape.82. The system recited in claim 74, wherein the incomplete recovery ischaracterized at least in part by a nadir measure of the at least onetime series.
 83. The system recited in claim 74, wherein the incompleterecovery is characterized at least in part by a clustering measure ofthe at least one time series.
 84. The system recited in claim 74,wherein the incomplete recovery is characterized at least in part by aperturbation measure of the at least one time series.
 85. The systemrecited in claim 74, wherein the incomplete recovery is characterized atleast in part by a recovery measure of the at least one time series. 86.A pulse oximeter, comprising: a probe that is adapted to be attached toa body part of a patient to create a signal indicative of an oxygensaturation of blood of the patient; and a processor that is adapted toreceive the signal produced by the probe, to calculate an SPO2 timeseries based on the signal, and to search for an incomplete recoveryrepresented by the SPO2 time series.
 87. The pulse oximeter recited inclaim 86, comprising an output device that is adapted to produce anoutput indicative of the incomplete recovery.
 88. The pulse oximeterrecited in claim 87, wherein the output device is adapted to update theoutput at a periodic interval.
 89. The pulse oximeter recited in claim87, wherein the output device is adapted to update the output at athreshold change point along the SPO2 time series.
 90. The pulseoximeter recited in claim 87, wherein the output device is adapted toupdate the output at a threshold change region along the SPO2 timeseries.
 91. The pulse oximeter recited in claim 86, comprising an outputdevice that is adapted to produce an output if the incomplete recoveryexceeds a threshold.
 92. The pulse oximeter recited in claim 86, whereinthe signal is derived from a plethesmographic pulse.
 93. The pulseoximeter recited in claim 86, wherein the incomplete recovery ischaracterized at least in part by a peak measure of the SPO2 timeseries.
 94. The pulse oximeter recited in claim 86, wherein theincomplete recovery is characterized at least in part by a nadir measureof the SPO2 time series.
 95. The pulse oximeter recited in claim 86,wherein the incomplete recovery is characterized at least in part by aclustering measure of the SPO2 time series.
 96. The pulse oximeterrecited in claim 86, wherein the incomplete recovery is characterized atleast in part by a perturbation measure of the SPO2 time series.
 97. Thepulse oximeter recited in claim 86, wherein the incomplete recovery ischaracterized at least in part by a recovery measure of the SPO2 timeseries.
 98. A system for analyzing data, comprising: means for receivingdata corresponding to at least one time series; and means for searchingthe data to identify an incomplete recovery.
 99. A tangiblemachine-readable medium, comprising: code adapted to access datacorresponding to at least one time series; and code adapted to searchthe data to identify an incomplete recovery.
 100. A patient monitor,comprising: a source of data corresponding to at least one time series;a processor that is adapted to detect an incomplete recoveries along theat least one time series, to compute an instability index value based atleast in part on the incomplete recovery, and to output an indication ofthe incomplete recovery and the instability index value.
 101. A methodof analyzing data, comprising: receiving data corresponding to at leastone time series; detecting at least one pattern in the data; andcomputing a plurality of sequential instability index values of the databased at least in part on the at least one pattern.
 102. The methodrecited in claim 94, comprising analyzing the pattern.
 103. A method ofanalyzing data, comprising: receiving data corresponding to at least onetime series; and detecting a plurality of pattern components of thedata; computing a plurality of sequential instability index values ofthe data based at least in part on at least one of the plurality ofpattern components.
 104. A method of analyzing data, comprising:receiving data corresponding to at least one time series; and detectinga plurality of abnormal values in the data; computing a plurality ofsequential instability index values of the data based at least in parton at least one of the plurality of abnormal values.
 105. A method ofanalyzing data, comprising: receiving data corresponding to at least onetime series; and detecting a plurality of abnormal values in the data;detecting at least one pattern of at least a subset of the abnormalvalues, computing a plurality of sequential instability index valuesbased at least in part on the detecting of the plurality of abnormalvalues and the at least one pattern.
 106. A method of analyzing datafrom a patient, comprising: receiving data corresponding to at least onetime series having at least one complex pattern; computing a pluralityof sequential instability index values indicative of an instability ofthe at least one pattern; and converting the plurality of sequentialinstability index values into an instability index time series.
 107. Amethod of analyzing data from a patient, comprising: receiving datacorresponding to at least one time series; and computing a plurality ofsequential instability index values indicative of a plurality ofsequential indications of a magnitude of instability of the patient.108. The method recited in claim 107, comprising converting theplurality of sequential instability index values into an instabilityindex value time series.
 109. A method of analyzing data from a patient,comprising: receiving data corresponding to at least one time series;and computing a plurality of sequential instability index valuesindicative of at least one pattern of magnitude of instability of thepatient.
 110. The method recited in claim 109, comprising converting theplurality of sequential instability index values into an instabilityindex value time series.